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46 | influenza | {"blocks": [{"args": [], "type": "graphers", "block": [{"note": "The share of positive tests is calculated by dividing the number of tests that were positive to any flu strain by three possible denominators, based on data availability. If both values are available then the denominator is the sum of positive and negative tests; if not then the number specimens processed is used; if neither of those are available then the number of specimens received by the testing facility is used.", "type": "LineChart", "title": "Weekly share of influenza tests that were positive", "ySlugs": "grapher__who__latest__flu__flu__pcnt_poscombined__51", "subtitle": "People who were tested had a respiratory infection with a fever, cough and onset within the last 10 days. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. 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Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. This shows the number of cases that were confirmed to have a strain of influenza A virus related to the H1N1 (\"\"Swine flu\"\") pandemic that emerged in 2009.\"", "Interval Radio": "Monthly", "Metric Dropdown": "Influenza A H1N12009", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Non-sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"type": "LineChart", "title": "Weekly confirmed cases of influenza A H1", "ySlugs": "grapher__who__latest__flu__flu__ah1combined__6", "subtitle": "This shows the number of cases that were confirmed to be of the H1 strain of influenza A virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Weekly", "Metric Dropdown": "Influenza A H1", "timelineMinTime": "-4043", "Surveillance type Dropdown": "All types", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"type": "LineChart", "title": "Monthly confirmed cases of influenza A H1", "ySlugs": "grapher__who__latest__flu__flu_monthly__ah1combined__63", "subtitle": "This shows the number of cases that were confirmed to be of the H1 strain of influenza A virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Monthly", "Metric Dropdown": "Influenza A H1", "timelineMinTime": "-4043", "Surveillance type Dropdown": "All types", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"type": "LineChart", "title": "Weekly confirmed cases of influenza A H3", "ySlugs": "grapher__who__latest__flu__flu__ah3combined__14", "subtitle": "This shows the number of cases that were confirmed to be of the H3 strain of influenza A virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Weekly", "Metric Dropdown": "Influenza A H3", "timelineMinTime": "-4043", "Surveillance type Dropdown": "All types", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "This shows the number of cases that were confirmed to be of the H3 strain of influenza A virus.", "type": "LineChart", "title": "Weekly confirmed cases of influenza A H3, sentinel surveillance", "ySlugs": "grapher__who__latest__flu__flu__ah3sentinel__18", "subtitle": "Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Weekly", "Metric Dropdown": "Influenza A H3", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "This shows the number of cases that were confirmed to be of the H3 strain of influenza A virus.", "type": "LineChart", "title": "Weekly confirmed cases of influenza A H3, non-sentinel surveillance", "ySlugs": "grapher__who__latest__flu__flu__ah3nonsentinel__16", "subtitle": "Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Weekly", "Metric Dropdown": "Influenza A H3", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Non-sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"type": "LineChart", "title": "Monthly confirmed cases of influenza A H3", "ySlugs": "grapher__who__latest__flu__flu_monthly__ah3combined__71", "subtitle": "This shows the number of cases that were confirmed to be of the H3 strain of influenza A virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Monthly", "Metric Dropdown": "Influenza A H3", "timelineMinTime": "-4043", "Surveillance type Dropdown": "All types", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "This shows the number of cases that were confirmed to be of the H3 strain of influenza A virus.", "type": "LineChart", "title": "Monthly confirmed cases of influenza A H3, sentinel surveillance", "ySlugs": "grapher__who__latest__flu__flu_monthly__ah3sentinel__75", "subtitle": "Sentinel surveillance consists of data routinely collected from sites representative of the country's population.. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Monthly", "Metric Dropdown": "Influenza A H3", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "This shows the number of cases that were confirmed to be of the H3 strain of influenza A virus.", "type": "LineChart", "title": "Monthly confirmed cases of influenza A H3, non-sentinel surveillance", "ySlugs": "grapher__who__latest__flu__flu_monthly__ah3nonsentinel__73", "subtitle": "Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Monthly", "Metric Dropdown": "Influenza A H3", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Non-sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"type": "LineChart", "title": "Weekly confirmed cases of influenza A H5", "ySlugs": "grapher__who__latest__flu__flu__ah5combined__20", "subtitle": "This shows the number of cases that were confirmed to be of the H5 strain of influenza A virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Weekly", "Metric Dropdown": "Influenza A H5", "timelineMinTime": "-4043", "relatedQuestionUrl": "https://ourworldindata.org/grapher/h5n1-flu-reported-cases", "relatedQuestionText": "How many cases of H5N1 have been reported?", "Surveillance type Dropdown": "All types", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"type": "LineChart", "title": "Monthly confirmed cases of influenza A H5", "ySlugs": "grapher__who__latest__flu__flu_monthly__ah5combined__77", "subtitle": "This shows the number of cases that were confirmed to be of the H5 strain of influenza A virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Monthly", "Metric Dropdown": "Influenza A H5", "timelineMinTime": "-4043", "relatedQuestionUrl": "https://ourworldindata.org/grapher/h5n1-flu-reported-cases", "relatedQuestionText": "How many cases of H5N1 have been reported?", "Surveillance type Dropdown": "All types", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"type": "LineChart", "title": "Weekly confirmed cases of influenza A H7N9", "ySlugs": "grapher__who__latest__flu__flu__ah7n9combined__22", "subtitle": "This shows the number of cases that were confirmed to be of the H7N9 strain of influenza A virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their subtype. The level of testing may vary between countries and over time.", "Interval Radio": "Weekly", "Metric Dropdown": "Influenza A H7N9", "timelineMinTime": "-4043", "Surveillance type Dropdown": "All types", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"type": "LineChart", "title": "Monthly confirmed cases of influenza A H7N9", "ySlugs": "grapher__who__latest__flu__flu_monthly__ah7n9combined__79", "subtitle": "This shows the number of cases that were confirmed to be of the H7N9 strain of influenza A virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Monthly", "Metric Dropdown": "Influenza A H7N9", "timelineMinTime": "-4043", "Surveillance type Dropdown": "All types", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"type": "LineChart", "title": "Weekly confirmed cases of influenza A (unknown subtype)", "ySlugs": "grapher__who__latest__flu__flu__a_no_subtypecombined__0", "subtitle": "This shows the number of cases that were confirmed to be influenza A virus, but their subtype was not confirmed. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Weekly", "Metric Dropdown": "Influenza A (unknown subtype)", "timelineMinTime": "-4043", "Surveillance type Dropdown": "All types", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "This shows the number of cases that were confirmed to be influenza A virus, but their subtype was not confirmed.", "type": "LineChart", "title": "Weekly confirmed cases of influenza A (unknown subtype), sentinel surveillance", "ySlugs": "grapher__who__latest__flu__flu__a_no_subtypesentinel__4", "subtitle": "Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Weekly", "Metric Dropdown": "Influenza A (unknown subtype)", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "This shows the number of cases that were confirmed to be influenza A virus, but their subtype was not confirmed.", "type": "LineChart", "title": "Weekly confirmed cases of influenza A (unknown subtype), non-sentinel surveillance", "ySlugs": "grapher__who__latest__flu__flu__a_no_subtypenonsentinel__2", "subtitle": "Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Weekly", "Metric Dropdown": "Influenza A (unknown subtype)", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Non-sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"type": "LineChart", "title": "Monthly confirmed cases of influenza A (unknown subtype)", "ySlugs": "grapher__who__latest__flu__flu_monthly__a_no_subtypecombined__57", "subtitle": "This shows the number of cases that were confirmed to be influenza A virus, but their subtype was not confirmed. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Monthly", "Metric Dropdown": "Influenza A (unknown subtype)", "timelineMinTime": "-4043", "Surveillance type Dropdown": "All types", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "This shows the number of cases that were confirmed to be influenza A virus, but their subtype was not confirmed.", "type": "LineChart", "title": "Monthly confirmed cases of influenza A (unknown subtype), sentinel surveillance", "ySlugs": "grapher__who__latest__flu__flu_monthly__a_no_subtypesentinel__61", "subtitle": "Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Monthly", "Metric Dropdown": "Influenza A (unknown subtype)", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "This shows the number of cases that were confirmed to be influenza A virus, but their subtype was not confirmed.", "type": "LineChart", "title": "Monthly confirmed cases of influenza A (unknown subtype), non-sentinel surveillance", "ySlugs": "grapher__who__latest__flu__flu_monthly__a_no_subtypenonsentinel__59", "subtitle": "Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Monthly", "Metric Dropdown": "Influenza A (unknown subtype)", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Non-sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"type": "LineChart", "title": "Weekly confirmed cases of influenza B", "ySlugs": "grapher__who__latest__flu__flu__inf_bcombined__48", "subtitle": "This includes confirmed cases of all strains of influenza B virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Weekly", "Metric Dropdown": "All influenza B", "timelineMinTime": "-4043", "Surveillance type Dropdown": "All types", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "This includes confirmed cases of all strains of influenza B virus.", "type": "LineChart", "title": "Weekly confirmed cases of influenza B, sentinel surveillance", "ySlugs": "grapher__who__latest__flu__flu__inf_bsentinel__50", "subtitle": "Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Weekly", "Metric Dropdown": "All influenza B", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "This includes confirmed cases of all strains of influenza B virus.", "type": "LineChart", "title": "Weekly confirmed cases of influenza B, non-sentinel surveillance", "ySlugs": "grapher__who__latest__flu__flu__inf_bnonsentinel__49", "subtitle": "Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Weekly", "Metric Dropdown": "All influenza B", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Non-sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"type": "LineChart", "title": "Monthly confirmed cases of influenza B", "ySlugs": "grapher__who__latest__flu__flu_monthly__inf_bcombined__105", "subtitle": "This includes confirmed cases of any lineage of influenza B virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their lineage. The level of testing may vary between countries and over time.", "Interval Radio": "Monthly", "Metric Dropdown": "All influenza B", "timelineMinTime": "-4043", "Surveillance type Dropdown": "All types", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "This includes confirmed cases of any lineage of influenza B virus.", "type": "LineChart", "title": "Monthly confirmed cases of influenza B, sentinel surveillance", "ySlugs": "grapher__who__latest__flu__flu_monthly__inf_bsentinel__107", "subtitle": "Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their lineage. The level of testing may vary between countries and over time.", "Interval Radio": "Monthly", "Metric Dropdown": "All influenza B", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "This includes confirmed cases of any lineage of influenza B virus.", "type": "LineChart", "title": "Monthly confirmed cases of influenza B, non-sentinel surveillance", "ySlugs": "grapher__who__latest__flu__flu_monthly__inf_bnonsentinel__106", "subtitle": "Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their lineage. The level of testing may vary between countries and over time.", "Interval Radio": "Monthly", "Metric Dropdown": "All influenza B", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Non-sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"type": "LineChart", "title": "Weekly confirmed cases of influenza B Victoria", "ySlugs": "grapher__who__latest__flu__flu__bviccombined__30", "subtitle": "This shows the number of cases that were confirmed to be of the Victoria lineage of influenza B virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Weekly", "Metric Dropdown": "Influenza B Victoria", "timelineMinTime": "-4043", "Surveillance type Dropdown": "All types", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "This shows the number of cases that were confirmed to be of the Victoria lineage of influenza B virus.", "type": "LineChart", "title": "Weekly confirmed cases of influenza B Victoria, sentinel surveillance", "ySlugs": "grapher__who__latest__flu__flu__bvicsentinel__34", "subtitle": "Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Weekly", "Metric Dropdown": "Influenza B Victoria", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "This shows the number of cases that were confirmed to be of the Victoria lineage of influenza B virus.", "type": "LineChart", "title": "Weekly confirmed cases of influenza B Victoria, non-sentinel surveillance", "ySlugs": "grapher__who__latest__flu__flu__bvicnonsentinel__32", "subtitle": "Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Weekly", "Metric Dropdown": "Influenza B Victoria", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Non-sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"type": "LineChart", "title": "Monthly confirmed cases of influenza B Victoria", "ySlugs": "grapher__who__latest__flu__flu_monthly__bviccombined__87", "subtitle": "This shows the number of cases that were confirmed to be of the Victoria lineage of influenza B virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Monthly", "Metric Dropdown": "Influenza B Victoria", "timelineMinTime": "-4043", "Surveillance type Dropdown": "All types", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "This shows the number of cases that were confirmed to be of the Victoria lineage of influenza B virus.", "type": "LineChart", "title": "Monthly confirmed cases of influenza B Victoria, sentinel surveillance", "ySlugs": "grapher__who__latest__flu__flu_monthly__bvicsentinel__91", "subtitle": "Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Monthly", "Metric Dropdown": "Influenza B Victoria", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "This shows the number of cases that were confirmed to be of the Victoria lineage of influenza B virus.", "type": "LineChart", "title": "Monthly confirmed cases of influenza B Victoria, non-sentinel surveillance", "ySlugs": "grapher__who__latest__flu__flu_monthly__bvicnonsentinel__89", "subtitle": "Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Monthly", "Metric Dropdown": "Influenza B Victoria", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Non-sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"type": "LineChart", "title": "Weekly confirmed cases of influenza B Yamagata,", "ySlugs": "grapher__who__latest__flu__flu__byamcombined__36", "subtitle": "This shows the number of cases that were confirmed to be of the Yamagata lineage of influenza B virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Weekly", "Metric Dropdown": "Influenza B Yamagata", "timelineMinTime": "-4043", "Surveillance type Dropdown": "All types", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "This shows the number of cases that were confirmed to be of the Yamagata lineage of influenza B virus.", "type": "LineChart", "title": "Weekly confirmed cases of influenza B Yamagata, sentinel surveillance", "ySlugs": "grapher__who__latest__flu__flu__byamsentinel__40", "subtitle": "Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Weekly", "Metric Dropdown": "Influenza B Yamagata", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "This shows the number of cases that were confirmed to be of the Yamagata lineage of influenza B virus.", "type": "LineChart", "title": "Weekly confirmed cases of influenza B Yamagata, non-sentinel surveillance", "ySlugs": "grapher__who__latest__flu__flu__byamnonsentinel__38", "subtitle": "Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Weekly", "Metric Dropdown": "Influenza B Yamagata", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Non-sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"type": "LineChart", "title": "Monthly confirmed cases of influenza B Yamagata", "ySlugs": "grapher__who__latest__flu__flu_monthly__byamcombined__93", "subtitle": "This shows the number of cases that were confirmed to be of the Yamagata lineage of influenza B virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Monthly", "Metric Dropdown": "Influenza B Yamagata", "timelineMinTime": "-4043", "Surveillance type Dropdown": "All types", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "This shows the number of cases that were confirmed to be of the Yamagata lineage of influenza B virus.", "type": "LineChart", "title": "Monthly confirmed cases of influenza B Yamagata, sentinel surveillance", "ySlugs": "grapher__who__latest__flu__flu_monthly__byamsentinel__97", "subtitle": "Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Monthly", "Metric Dropdown": "Influenza B Yamagata", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "This shows the number of cases that were confirmed to be of the Yamagata lineage of influenza B virus.", "type": "LineChart", "title": "Monthly confirmed cases of influenza B Yamagata, non-sentinel surveillance", "ySlugs": "grapher__who__latest__flu__flu_monthly__byamnonsentinel__95", "subtitle": "Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Monthly", "Metric Dropdown": "Influenza B Yamagata", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Non-sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"type": "LineChart", "title": "Weekly confirmed cases of influenza B (unknown subtype)", "ySlugs": "grapher__who__latest__flu__flu__bnotdeterminedcombined__24", "subtitle": "This shows the number of cases that were confirmed to be influenza B virus, but their lineage was not determined. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Weekly", "Metric Dropdown": "Influenza B (unknown lineage)", "timelineMinTime": "-4043", "Surveillance type Dropdown": "All types", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "This shows the number of cases that were confirmed to be influenza B virus, but their lineage was not determined.", "type": "LineChart", "title": "Weekly confirmed cases of influenza B (unknown subtype), sentinel surveillance", "ySlugs": "grapher__who__latest__flu__flu__bnotdeterminedsentinel__28", "subtitle": "Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Weekly", "Metric Dropdown": "Influenza B (unknown lineage)", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "This shows the number of cases that were confirmed to be influenza B virus, but their lineage was not determined.", "type": "LineChart", "title": "Weekly confirmed cases of influenza B (unknown subtype), non-sentinel surveillance", "ySlugs": "grapher__who__latest__flu__flu__bnotdeterminednonsentinel__26", "subtitle": "Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Weekly", "Metric Dropdown": "Influenza B (unknown lineage)", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Non-sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"type": "LineChart", "title": "Monthly confirmed cases of influenza B (unknown subtype)", "ySlugs": "grapher__who__latest__flu__flu_monthly__bnotdeterminedcombined__81", "subtitle": "This shows the number of cases that were confirmed to be influenza B virus, but their lineage was not determined. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Monthly", "Metric Dropdown": "Influenza B (unknown lineage)", "timelineMinTime": "-4043", "Surveillance type Dropdown": "All types", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "This shows the number of cases that were confirmed to be influenza B virus, but their lineage was not determined.", "type": "LineChart", "title": "Monthly confirmed cases of influenza B (unknown subtype), sentinel surveillance", "ySlugs": "grapher__who__latest__flu__flu_monthly__bnotdeterminedsentinel__85", "subtitle": "Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Monthly", "Metric Dropdown": "Influenza B (unknown lineage)", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "This shows the number of cases that were confirmed to be influenza B virus, but their lineage was not determined.", "type": "LineChart", "title": "Monthly confirmed cases of influenza B (unknown subtype), non-sentinel surveillance", "ySlugs": "grapher__who__latest__flu__flu_monthly__bnotdeterminednonsentinel__83", "subtitle": "Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "Interval Radio": "Monthly", "Metric Dropdown": "Influenza B (unknown lineage)", "timelineMinTime": "-4043", "Surveillance type Dropdown": "Non-sentinel surveillance", "Confirmed cases or Symptoms Radio": "Confirmed cases"}, {"note": "Acute respiratory illnesses are defined by the WHO as sudden/acute onset of ≥1 of the following symptoms: cough, sore throat, shortness of breath, rhinitis; and were judged by a clinician to be due to an infection. Influenza-like illnesses refer to acute respiratory infections with a fever ≥38ºC, a cough, and onset of symptoms within the last 10 days. Severe acute respiratory infections refer to serious cases of influenza-like illnesses which require hospitalization. This can include diseases other than influenza, such as COVID-19.", "type": "LineChart", "title": "Weekly comparison of data on respiratory infections", "ySlugs": "grapher__who__latest__flu__flu__reported_ari_cases__54 grapher__who__latest__flu__flu__reported_ili_cases__55 grapher__who__latest__flu__flu__reported_sari_cases__56", "subtitle": "Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time.", "hasMapTab": "false", "facetYDomain": "independent", "Interval Radio": "Weekly", "Metric Dropdown": "Comparison of data on respiratory infections", "timelineMinTime": "-4043", "selectedFacetStrategy": "metric", "Confirmed cases or Symptoms Radio": "Symptoms"}, {"note": "Acute respiratory illnesses are defined by the WHO as sudden/acute onset of ≥1 of the following symptoms: cough, sore throat, shortness of breath, rhinitis; and were judged by a clinician to be due to an infection. Influenza-like illnesses refer to acute respiratory infections with a fever ≥38ºC, a cough, and onset of symptoms within the last 10 days. Severe acute respiratory in |
2023-07-19 13:24:32 | 2025-04-22 06:35:57 | 74 | 2025-04-22 06:35:57 | Update explorer from ETL | # DO NOT EDIT THIS FILE MANUALLY. IT WAS GENERATED BY ETL step 'who/latest/influenza#influenza'. explorerTitle Influenza isPublished true explorerSubtitle Explore global data produced by the World Health Organization on influenza symptoms and cases. selection Northern Hemisphere Southern Hemisphere hideAlertBanner true hideAnnotationFieldsInTitle true yAxisMin 0 wpBlockId 56732 hasMapTab true pickerColumnSlugs Country graphers yVariableIds ySlugs Confirmed cases or Symptoms Radio Metric Dropdown Interval Radio Surveillance type Dropdown timelineMinTime title subtitle type note defaultView tab selectedFacetStrategy facetYDomain hasMapTab relatedQuestionText relatedQuestionUrl grapher__who__latest__flu__flu__pcnt_poscombined__51 Confirmed cases Share of positive tests (%) Weekly All types -4043 Weekly share of influenza tests that were positive People who were tested had a respiratory infection with a fever, cough and onset within the last 10 days. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart The share of positive tests is calculated by dividing the number of tests that were positive to any flu strain by three possible denominators, based on data availability. If both values are available then the denominator is the sum of positive and negative tests; if not then the number specimens processed is used; if neither of those are available then the number of specimens received by the testing facility is used. grapher__who__latest__flu__flu__pcnt_possentinel__53 Confirmed cases Share of positive tests (%) Weekly Sentinel surveillance -4043 Weekly share of influenza tests that were positive, sentinel surveillance Sentinel surveillance consists of data routinely collected from sites representative of the country's population. People who were tested had a respiratory infection with a fever, cough and onset within the last 10 days. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart The share of positive tests is calculated by dividing the number of tests that were positive to any flu strain by three possible denominators, based on data availability. If both values are available then the denominator is the sum of positive and negative tests; if not then the number specimens processed is used; if neither of those are available then the number of specimens received by the testing facility is used. grapher__who__latest__flu__flu__pcnt_posnonsentinel__52 Confirmed cases Share of positive tests (%) Weekly Non-sentinel surveillance -4043 Weekly share of influenza tests that were positive, non-sentinel surveillance Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. People who were tested had a respiratory infection with a fever, cough and onset within the last 10 days. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart The share of positive tests is calculated by dividing the number of tests that were positive to any flu strain by three possible denominators, based on data availability. If both values are available then the denominator is the sum of positive and negative tests; if not then the number specimens processed is used; if neither of those are available then the number of specimens received by the testing facility is used. grapher__who__latest__flu__flu_monthly__pcnt_poscombined__108 Confirmed cases Share of positive tests (%) Monthly All types -4043 Monthly share of influenza tests that were positive People who were tested had a respiratory infection with a fever, cough and onset within the last 10 days. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart The share of positive tests is calculated by dividing the number of tests that were positive to any flu strain by three possible denominators, based on data availability. If both values are available then the denominator is the sum of positive and negative tests; if not then the number specimens processed is used; if neither of those are available then the number of specimens received by the testing facility is used. true map grapher__who__latest__flu__flu_monthly__pcnt_possentinel__110 Confirmed cases Share of positive tests (%) Monthly Sentinel surveillance -4043 Monthly share of influenza tests that were positive, sentinel surveillance Sentinel surveillance consists of data routinely collected from sites representative of the country's population. People who were tested had a respiratory infection with a fever, cough and onset within the last 10 days. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart The share of positive tests is calculated by dividing the number of tests that were positive to any flu strain by three possible denominators, based on data availability. If both values are available then the denominator is the sum of positive and negative tests; if not then the number specimens processed is used; if neither of those are available then the number of specimens received by the testing facility is used. grapher__who__latest__flu__flu_monthly__pcnt_posnonsentinel__109 Confirmed cases Share of positive tests (%) Monthly Non-sentinel surveillance -4043 Monthly share of influenza tests that were positive, non-sentinel surveillance Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. People who were tested had a respiratory infection with a fever, cough and onset within the last 10 days. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart The share of positive tests is calculated by dividing the number of tests that were positive to any flu strain by three possible denominators, based on data availability. If both values are available then the denominator is the sum of positive and negative tests; if not then the number specimens processed is used; if neither of those are available then the number of specimens received by the testing facility is used. grapher__who__latest__flu__flu__ah1n12009combined_zfilled__9 grapher__who__latest__flu__flu__ah1combined_zfilled__7 grapher__who__latest__flu__flu__ah3combined_zfilled__15 grapher__who__latest__flu__flu__ah5combined_zfilled__21 grapher__who__latest__flu__flu__ah7n9combined_zfilled__23 grapher__who__latest__flu__flu__a_no_subtypecombined_zfilled__1 grapher__who__latest__flu__flu__bviccombined_zfilled__31 grapher__who__latest__flu__flu__byamcombined_zfilled__37 grapher__who__latest__flu__flu__bnotdeterminedcombined_zfilled__25 Confirmed cases Confirmed cases (by strain) Weekly All types -4043 Weekly confirmed cases of influenza by strain People who were tested had a respiratory infection with a fever, cough and onset within the last 10 days. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. StackedArea entity independent false grapher__who__latest__flu__flu__ah1n12009sentinel_zfilled__13 grapher__who__latest__flu__flu__ah3sentinel_zfilled__19 grapher__who__latest__flu__flu__a_no_subtypesentinel_zfilled__5 grapher__who__latest__flu__flu__bvicsentinel_zfilled__35 grapher__who__latest__flu__flu__byamsentinel_zfilled__41 grapher__who__latest__flu__flu__bnotdeterminedsentinel_zfilled__29 Confirmed cases Confirmed cases (by strain) Weekly Sentinel surveillance -4043 Weekly confirmed cases of influenza by strain, sentinel surveillance Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. StackedArea entity independent false grapher__who__latest__flu__flu__ah1n12009nonsentinel_zfilled__11 grapher__who__latest__flu__flu__ah3nonsentinel_zfilled__17 grapher__who__latest__flu__flu__a_no_subtypenonsentinel_zfilled__3 grapher__who__latest__flu__flu__bvicnonsentinel_zfilled__33 grapher__who__latest__flu__flu__byamnonsentinel_zfilled__39 grapher__who__latest__flu__flu__bnotdeterminednonsentinel_zfilled__27 Confirmed cases Confirmed cases (by strain) Weekly Non-sentinel surveillance -4043 Weekly confirmed cases of influenza by strain, non-sentinel surveillance Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. StackedArea entity independent false grapher__who__latest__flu__flu_monthly__ah1n12009combined_zfilled__66 grapher__who__latest__flu__flu_monthly__ah1combined_zfilled__64 grapher__who__latest__flu__flu_monthly__ah3combined_zfilled__72 grapher__who__latest__flu__flu_monthly__ah5combined_zfilled__78 grapher__who__latest__flu__flu_monthly__ah7n9combined_zfilled__80 grapher__who__latest__flu__flu_monthly__a_no_subtypecombined_zfilled__58 grapher__who__latest__flu__flu_monthly__bviccombined_zfilled__88 grapher__who__latest__flu__flu_monthly__byamcombined_zfilled__94 grapher__who__latest__flu__flu_monthly__bnotdeterminedcombined_zfilled__82 Confirmed cases Confirmed cases (by strain) Monthly All types -4043 Monthly confirmed cases of influenza by strain People who were tested had a respiratory infection with a fever, cough and onset within the last 10 days. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. StackedArea entity independent false grapher__who__latest__flu__flu_monthly__ah1n12009sentinel_zfilled__70 grapher__who__latest__flu__flu_monthly__ah3sentinel_zfilled__76 grapher__who__latest__flu__flu_monthly__a_no_subtypesentinel_zfilled__62 grapher__who__latest__flu__flu_monthly__bvicsentinel_zfilled__92 grapher__who__latest__flu__flu_monthly__byamsentinel_zfilled__98 grapher__who__latest__flu__flu_monthly__bnotdeterminedsentinel_zfilled__86 Confirmed cases Confirmed cases (by strain) Monthly Sentinel surveillance -4043 Monthly confirmed cases of influenza by strain, sentinel surveillance Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. StackedArea entity independent false grapher__who__latest__flu__flu_monthly__ah1n12009nonsentinel_zfilled__68 grapher__who__latest__flu__flu_monthly__ah3nonsentinel_zfilled__74 grapher__who__latest__flu__flu_monthly__a_no_subtypenonsentinel_zfilled__60 grapher__who__latest__flu__flu_monthly__bvicnonsentinel_zfilled__90 grapher__who__latest__flu__flu_monthly__byamnonsentinel_zfilled__96 grapher__who__latest__flu__flu_monthly__bnotdeterminednonsentinel_zfilled__84 Confirmed cases Confirmed cases (by strain) Monthly Non-sentinel surveillance -4043 Monthly confirmed cases of influenza by strain, non-sentinel surveillance Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. StackedArea entity independent false grapher__who__latest__flu__flu__inf_allcombined__43 grapher__who__latest__flu__flu__inf_allsentinel__45 grapher__who__latest__flu__flu__inf_allnonsentinel__44 Confirmed cases Confirmed cases (by surveillance type) Weekly -4043 Weekly confirmed cases of influenza by surveillance type This includes confirmed cases of all influenza strains. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart Sentinel surveillance consists of data routinely collected from sites representative of the country's population.\nNon-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. metric independent false grapher__who__latest__flu__flu_monthly__inf_allcombined__100 grapher__who__latest__flu__flu_monthly__inf_allsentinel__102 grapher__who__latest__flu__flu_monthly__inf_allnonsentinel__101 Confirmed cases Confirmed cases (by surveillance type) Monthly -4043 Monthly confirmed cases of influenza by surveillance type This includes confirmed cases of all influenza strains. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart Sentinel surveillance consists of data routinely collected from sites representative of the country's population.\nNon-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. metric independent false grapher__who__latest__flu__flu__inf_allcombined__43 Confirmed cases All influenza strains Weekly All types -4043 Weekly confirmed cases of influenza This includes confirmed cases of all influenza strains. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart grapher__who__latest__flu__flu__inf_allsentinel__45 Confirmed cases All influenza strains Weekly Sentinel surveillance -4043 Weekly confirmed cases of influenza, sentinel surveillance Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This includes confirmed cases of all influenza strains. grapher__who__latest__flu__flu__inf_allnonsentinel__44 Confirmed cases All influenza strains Weekly Non-sentinel surveillance -4043 Weekly confirmed cases of influenza, non-sentinel surveillance Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This includes confirmed cases of all influenza strains. grapher__who__latest__flu__flu_monthly__inf_allcombined__100 Confirmed cases All influenza strains Monthly All types -4043 Monthly confirmed cases of influenza This includes confirmed cases of all influenza strains. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart grapher__who__latest__flu__flu_monthly__inf_allsentinel__102 Confirmed cases All influenza strains Monthly Sentinel surveillance -4043 Monthly confirmed cases of, sentinel surveillance Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This includes confirmed cases of all influenza strains. grapher__who__latest__flu__flu_monthly__inf_allnonsentinel__101 Confirmed cases All influenza strains Monthly Non-sentinel surveillance -4043 Monthly confirmed cases of influenza, non-sentinel surveillance Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This includes confirmed cases of all influenza strains. grapher__who__latest__flu__flu__inf_acombined__42 Confirmed cases All influenza A Weekly All types -4043 Weekly confirmed cases of influenza A This includes confirmed cases of all strains of influenza A virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart grapher__who__latest__flu__flu__inf_asentinel__47 Confirmed cases All influenza A Weekly Sentinel surveillance -4043 Weekly confirmed cases of influenza A, sentinel surveillance Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This includes confirmed cases of all strains of influenza A virus. grapher__who__latest__flu__flu__inf_anonsentinel__46 Confirmed cases All influenza A Weekly Non-sentinel surveillance -4043 Weekly confirmed cases of influenza A, non-sentinel surveillance Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This includes confirmed cases of all strains of influenza A virus. grapher__who__latest__flu__flu_monthly__inf_acombined__99 Confirmed cases All influenza A Monthly All types -4043 Monthly confirmed cases of influenza A This includes confirmed cases of all strains of influenza A virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart grapher__who__latest__flu__flu_monthly__inf_asentinel__104 Confirmed cases All influenza A Monthly Sentinel surveillance -4043 Monthly confirmed cases of influenza A, sentinel surveillance Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This includes confirmed cases of all strains of influenza A virus. grapher__who__latest__flu__flu_monthly__inf_anonsentinel__103 Confirmed cases All influenza A Monthly Non-sentinel surveillance -4043 Monthly confirmed cases of influenza A, non-sentinel surveillance Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This includes confirmed cases of all strains of influenza A virus. grapher__who__latest__flu__flu__ah1n12009combined__8 Confirmed cases Influenza A H1N12009 Weekly All types -4043 Weekly confirmed cases of influenza A H1N1 (2009) "This shows the number of cases that were confirmed to have a strain of influenza A virus related to the H1N1 (""Swine flu"") pandemic that emerged in 2009. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time." LineChart grapher__who__latest__flu__flu__ah1n12009sentinel__12 Confirmed cases Influenza A H1N12009 Weekly Sentinel surveillance -4043 Weekly confirmed cases of influenza A H1N1 (2009), sentinel surveillance "Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. This shows the number of cases that were confirmed to have a strain of influenza A virus related to the H1N1 (""Swine flu"") pandemic that emerged in 2009." LineChart "This shows the number of cases that were confirmed to have a strain of influenza A virus related to the H1N1 (""Swine flu"") pandemic that emerged in 2009." grapher__who__latest__flu__flu__ah1n12009nonsentinel__10 Confirmed cases Influenza A H1N12009 Weekly Non-sentinel surveillance -4043 Weekly confirmed cases of influenza A H1N1 (2009), non-sentinel surveillance "Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. This shows the number of cases that were confirmed to have a strain of influenza A virus related to the H1N1 (""Swine flu"") pandemic that emerged in 2009." LineChart "This shows the number of cases that were confirmed to have a strain of influenza A virus related to the H1N1 (""Swine flu"") pandemic that emerged in 2009." grapher__who__latest__flu__flu_monthly__ah1n12009combined__65 Confirmed cases Influenza A H1N12009 Monthly All types -4043 Monthly confirmed cases of influenza A H1N1 (2009) "This shows the number of cases that were confirmed to have a strain of influenza A virus related to the H1N1 (""Swine flu"") pandemic that emerged in 2009. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time." LineChart grapher__who__latest__flu__flu_monthly__ah1n12009sentinel__69 Confirmed cases Influenza A H1N12009 Monthly Sentinel surveillance -4043 Monthly confirmed cases of influenza A H1N1 (2009), sentinel surveillance Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart "This shows the number of cases that were confirmed to have a strain of influenza A virus related to the H1N1 (""Swine flu"") pandemic that emerged in 2009." grapher__who__latest__flu__flu_monthly__ah1n12009nonsentinel__67 Confirmed cases Influenza A H1N12009 Monthly Non-sentinel surveillance -4043 Monthly confirmed cases of influenza A H1N1 (2009), non-sentinel surveillance "Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. This shows the number of cases that were confirmed to have a strain of influenza A virus related to the H1N1 (""Swine flu"") pandemic that emerged in 2009." LineChart "This shows the number of cases that were confirmed to have a strain of influenza A virus related to the H1N1 (""Swine flu"") pandemic that emerged in 2009." grapher__who__latest__flu__flu__ah1combined__6 Confirmed cases Influenza A H1 Weekly All types -4043 Weekly confirmed cases of influenza A H1 This shows the number of cases that were confirmed to be of the H1 strain of influenza A virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart grapher__who__latest__flu__flu_monthly__ah1combined__63 Confirmed cases Influenza A H1 Monthly All types -4043 Monthly confirmed cases of influenza A H1 This shows the number of cases that were confirmed to be of the H1 strain of influenza A virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart grapher__who__latest__flu__flu__ah3combined__14 Confirmed cases Influenza A H3 Weekly All types -4043 Weekly confirmed cases of influenza A H3 This shows the number of cases that were confirmed to be of the H3 strain of influenza A virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart grapher__who__latest__flu__flu__ah3sentinel__18 Confirmed cases Influenza A H3 Weekly Sentinel surveillance -4043 Weekly confirmed cases of influenza A H3, sentinel surveillance Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This shows the number of cases that were confirmed to be of the H3 strain of influenza A virus. grapher__who__latest__flu__flu__ah3nonsentinel__16 Confirmed cases Influenza A H3 Weekly Non-sentinel surveillance -4043 Weekly confirmed cases of influenza A H3, non-sentinel surveillance Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This shows the number of cases that were confirmed to be of the H3 strain of influenza A virus. grapher__who__latest__flu__flu_monthly__ah3combined__71 Confirmed cases Influenza A H3 Monthly All types -4043 Monthly confirmed cases of influenza A H3 This shows the number of cases that were confirmed to be of the H3 strain of influenza A virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart grapher__who__latest__flu__flu_monthly__ah3sentinel__75 Confirmed cases Influenza A H3 Monthly Sentinel surveillance -4043 Monthly confirmed cases of influenza A H3, sentinel surveillance Sentinel surveillance consists of data routinely collected from sites representative of the country's population.. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This shows the number of cases that were confirmed to be of the H3 strain of influenza A virus. grapher__who__latest__flu__flu_monthly__ah3nonsentinel__73 Confirmed cases Influenza A H3 Monthly Non-sentinel surveillance -4043 Monthly confirmed cases of influenza A H3, non-sentinel surveillance Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This shows the number of cases that were confirmed to be of the H3 strain of influenza A virus. grapher__who__latest__flu__flu__ah5combined__20 Confirmed cases Influenza A H5 Weekly All types -4043 Weekly confirmed cases of influenza A H5 This shows the number of cases that were confirmed to be of the H5 strain of influenza A virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart How many cases of H5N1 have been reported? https://ourworldindata.org/grapher/h5n1-flu-reported-cases grapher__who__latest__flu__flu_monthly__ah5combined__77 Confirmed cases Influenza A H5 Monthly All types -4043 Monthly confirmed cases of influenza A H5 This shows the number of cases that were confirmed to be of the H5 strain of influenza A virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart How many cases of H5N1 have been reported? https://ourworldindata.org/grapher/h5n1-flu-reported-cases grapher__who__latest__flu__flu__ah7n9combined__22 Confirmed cases Influenza A H7N9 Weekly All types -4043 Weekly confirmed cases of influenza A H7N9 This shows the number of cases that were confirmed to be of the H7N9 strain of influenza A virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their subtype. The level of testing may vary between countries and over time. LineChart grapher__who__latest__flu__flu_monthly__ah7n9combined__79 Confirmed cases Influenza A H7N9 Monthly All types -4043 Monthly confirmed cases of influenza A H7N9 This shows the number of cases that were confirmed to be of the H7N9 strain of influenza A virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart grapher__who__latest__flu__flu__a_no_subtypecombined__0 Confirmed cases Influenza A (unknown subtype) Weekly All types -4043 Weekly confirmed cases of influenza A (unknown subtype) This shows the number of cases that were confirmed to be influenza A virus, but their subtype was not confirmed. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart grapher__who__latest__flu__flu__a_no_subtypesentinel__4 Confirmed cases Influenza A (unknown subtype) Weekly Sentinel surveillance -4043 Weekly confirmed cases of influenza A (unknown subtype), sentinel surveillance Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This shows the number of cases that were confirmed to be influenza A virus, but their subtype was not confirmed. grapher__who__latest__flu__flu__a_no_subtypenonsentinel__2 Confirmed cases Influenza A (unknown subtype) Weekly Non-sentinel surveillance -4043 Weekly confirmed cases of influenza A (unknown subtype), non-sentinel surveillance Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This shows the number of cases that were confirmed to be influenza A virus, but their subtype was not confirmed. grapher__who__latest__flu__flu_monthly__a_no_subtypecombined__57 Confirmed cases Influenza A (unknown subtype) Monthly All types -4043 Monthly confirmed cases of influenza A (unknown subtype) This shows the number of cases that were confirmed to be influenza A virus, but their subtype was not confirmed. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart grapher__who__latest__flu__flu_monthly__a_no_subtypesentinel__61 Confirmed cases Influenza A (unknown subtype) Monthly Sentinel surveillance -4043 Monthly confirmed cases of influenza A (unknown subtype), sentinel surveillance Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This shows the number of cases that were confirmed to be influenza A virus, but their subtype was not confirmed. grapher__who__latest__flu__flu_monthly__a_no_subtypenonsentinel__59 Confirmed cases Influenza A (unknown subtype) Monthly Non-sentinel surveillance -4043 Monthly confirmed cases of influenza A (unknown subtype), non-sentinel surveillance Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This shows the number of cases that were confirmed to be influenza A virus, but their subtype was not confirmed. grapher__who__latest__flu__flu__inf_bcombined__48 Confirmed cases All influenza B Weekly All types -4043 Weekly confirmed cases of influenza B This includes confirmed cases of all strains of influenza B virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart grapher__who__latest__flu__flu__inf_bsentinel__50 Confirmed cases All influenza B Weekly Sentinel surveillance -4043 Weekly confirmed cases of influenza B, sentinel surveillance Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This includes confirmed cases of all strains of influenza B virus. grapher__who__latest__flu__flu__inf_bnonsentinel__49 Confirmed cases All influenza B Weekly Non-sentinel surveillance -4043 Weekly confirmed cases of influenza B, non-sentinel surveillance Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This includes confirmed cases of all strains of influenza B virus. grapher__who__latest__flu__flu_monthly__inf_bcombined__105 Confirmed cases All influenza B Monthly All types -4043 Monthly confirmed cases of influenza B This includes confirmed cases of any lineage of influenza B virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their lineage. The level of testing may vary between countries and over time. LineChart grapher__who__latest__flu__flu_monthly__inf_bsentinel__107 Confirmed cases All influenza B Monthly Sentinel surveillance -4043 Monthly confirmed cases of influenza B, sentinel surveillance Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their lineage. The level of testing may vary between countries and over time. LineChart This includes confirmed cases of any lineage of influenza B virus. grapher__who__latest__flu__flu_monthly__inf_bnonsentinel__106 Confirmed cases All influenza B Monthly Non-sentinel surveillance -4043 Monthly confirmed cases of influenza B, non-sentinel surveillance Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their lineage. The level of testing may vary between countries and over time. LineChart This includes confirmed cases of any lineage of influenza B virus. grapher__who__latest__flu__flu__bviccombined__30 Confirmed cases Influenza B Victoria Weekly All types -4043 Weekly confirmed cases of influenza B Victoria This shows the number of cases that were confirmed to be of the Victoria lineage of influenza B virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart grapher__who__latest__flu__flu__bvicsentinel__34 Confirmed cases Influenza B Victoria Weekly Sentinel surveillance -4043 Weekly confirmed cases of influenza B Victoria, sentinel surveillance Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This shows the number of cases that were confirmed to be of the Victoria lineage of influenza B virus. grapher__who__latest__flu__flu__bvicnonsentinel__32 Confirmed cases Influenza B Victoria Weekly Non-sentinel surveillance -4043 Weekly confirmed cases of influenza B Victoria, non-sentinel surveillance Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This shows the number of cases that were confirmed to be of the Victoria lineage of influenza B virus. grapher__who__latest__flu__flu_monthly__bviccombined__87 Confirmed cases Influenza B Victoria Monthly All types -4043 Monthly confirmed cases of influenza B Victoria This shows the number of cases that were confirmed to be of the Victoria lineage of influenza B virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart grapher__who__latest__flu__flu_monthly__bvicsentinel__91 Confirmed cases Influenza B Victoria Monthly Sentinel surveillance -4043 Monthly confirmed cases of influenza B Victoria, sentinel surveillance Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This shows the number of cases that were confirmed to be of the Victoria lineage of influenza B virus. grapher__who__latest__flu__flu_monthly__bvicnonsentinel__89 Confirmed cases Influenza B Victoria Monthly Non-sentinel surveillance -4043 Monthly confirmed cases of influenza B Victoria, non-sentinel surveillance Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This shows the number of cases that were confirmed to be of the Victoria lineage of influenza B virus. grapher__who__latest__flu__flu__byamcombined__36 Confirmed cases Influenza B Yamagata Weekly All types -4043 Weekly confirmed cases of influenza B Yamagata, This shows the number of cases that were confirmed to be of the Yamagata lineage of influenza B virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart grapher__who__latest__flu__flu__byamsentinel__40 Confirmed cases Influenza B Yamagata Weekly Sentinel surveillance -4043 Weekly confirmed cases of influenza B Yamagata, sentinel surveillance Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This shows the number of cases that were confirmed to be of the Yamagata lineage of influenza B virus. grapher__who__latest__flu__flu__byamnonsentinel__38 Confirmed cases Influenza B Yamagata Weekly Non-sentinel surveillance -4043 Weekly confirmed cases of influenza B Yamagata, non-sentinel surveillance Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This shows the number of cases that were confirmed to be of the Yamagata lineage of influenza B virus. grapher__who__latest__flu__flu_monthly__byamcombined__93 Confirmed cases Influenza B Yamagata Monthly All types -4043 Monthly confirmed cases of influenza B Yamagata This shows the number of cases that were confirmed to be of the Yamagata lineage of influenza B virus. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart grapher__who__latest__flu__flu_monthly__byamsentinel__97 Confirmed cases Influenza B Yamagata Monthly Sentinel surveillance -4043 Monthly confirmed cases of influenza B Yamagata, sentinel surveillance Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This shows the number of cases that were confirmed to be of the Yamagata lineage of influenza B virus. grapher__who__latest__flu__flu_monthly__byamnonsentinel__95 Confirmed cases Influenza B Yamagata Monthly Non-sentinel surveillance -4043 Monthly confirmed cases of influenza B Yamagata, non-sentinel surveillance Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This shows the number of cases that were confirmed to be of the Yamagata lineage of influenza B virus. grapher__who__latest__flu__flu__bnotdeterminedcombined__24 Confirmed cases Influenza B (unknown lineage) Weekly All types -4043 Weekly confirmed cases of influenza B (unknown subtype) This shows the number of cases that were confirmed to be influenza B virus, but their lineage was not determined. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart grapher__who__latest__flu__flu__bnotdeterminedsentinel__28 Confirmed cases Influenza B (unknown lineage) Weekly Sentinel surveillance -4043 Weekly confirmed cases of influenza B (unknown subtype), sentinel surveillance Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This shows the number of cases that were confirmed to be influenza B virus, but their lineage was not determined. grapher__who__latest__flu__flu__bnotdeterminednonsentinel__26 Confirmed cases Influenza B (unknown lineage) Weekly Non-sentinel surveillance -4043 Weekly confirmed cases of influenza B (unknown subtype), non-sentinel surveillance Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This shows the number of cases that were confirmed to be influenza B virus, but their lineage was not determined. grapher__who__latest__flu__flu_monthly__bnotdeterminedcombined__81 Confirmed cases Influenza B (unknown lineage) Monthly All types -4043 Monthly confirmed cases of influenza B (unknown subtype) This shows the number of cases that were confirmed to be influenza B virus, but their lineage was not determined. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart grapher__who__latest__flu__flu_monthly__bnotdeterminedsentinel__85 Confirmed cases Influenza B (unknown lineage) Monthly Sentinel surveillance -4043 Monthly confirmed cases of influenza B (unknown subtype), sentinel surveillance Sentinel surveillance consists of data routinely collected from sites representative of the country's population. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This shows the number of cases that were confirmed to be influenza B virus, but their lineage was not determined. grapher__who__latest__flu__flu_monthly__bnotdeterminednonsentinel__83 Confirmed cases Influenza B (unknown lineage) Monthly Non-sentinel surveillance -4043 Monthly confirmed cases of influenza B (unknown subtype), non-sentinel surveillance Non-sentinel surveillance includes data from multiple sources, including outbreak investigation, universal testing, and testing at the point of care. Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart This shows the number of cases that were confirmed to be influenza B virus, but their lineage was not determined. grapher__who__latest__flu__flu__reported_ari_cases__54 grapher__who__latest__flu__flu__reported_ili_cases__55 grapher__who__latest__flu__flu__reported_sari_cases__56 Symptoms Comparison of data on respiratory infections Weekly -4043 Weekly comparison of data on respiratory infections Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart Acute respiratory illnesses are defined by the WHO as sudden/acute onset of ≥1 of the following symptoms: cough, sore throat, shortness of breath, rhinitis; and were judged by a clinician to be due to an infection. Influenza-like illnesses refer to acute respiratory infections with a fever ≥38ºC, a cough, and onset of symptoms within the last 10 days. Severe acute respiratory infections refer to serious cases of influenza-like illnesses which require hospitalization. This can include diseases other than influenza, such as COVID-19. metric independent false grapher__who__latest__flu__flu_monthly__reported_ari_cases__111 grapher__who__latest__flu__flu_monthly__reported_ili_cases__112 grapher__who__latest__flu__flu_monthly__reported_sari_cases__113 Symptoms Comparison of data on respiratory infections Monthly -4043 Monthly comparison of data on respiratory infections Only a fraction of potential cases of influenza are tested by labs to confirm whether they have influenza and to identify their strain. The level of testing may vary between countries and over time. LineChart Acute respiratory illnesses are defined by the WHO as sudden/acute onset of ≥1 of the following symptoms: cough, sore throat, shortness of breath, rhinitis; and were judged by a clinician to be due to an infection. Influenza-like illnesses refer to acute respiratory infections with a fever ≥38ºC, a cough, and onset of symptoms within the last 10 days. Severe acute respiratory infections refer to serious cases of influenza-like illnesses which require hospitalization. This can include diseases other than influenza, such as COVID-19. metric independent false grapher__who__latest__flu__flu__reported_ili_cases__55 Symptoms Influenza-like illnesses Weekly -4043 Weekly reported cases of influenza-like illnesses Influenza-like illnesses are defined by the WHO as acute respiratory infections with a fever ≥38ºC, a cough, and onset of symptoms within the last 10 days. This can include diseases other than influenza, such as COVID-19. LineChart grapher__who__latest__flu__flu_monthly__reported_ili_cases__112 Symptoms Influenza-like illnesses Monthly -4043 Monthly reported cases of influenza-like illnesses Influenza-like illnesses are defined by the WHO as acute respiratory infections with a fever ≥38ºC, a cough, and onset of symptoms within the last 10 days. This can include diseases other than influenza, such as COVID-19. LineChart grapher__who__latest__flu__flu__reported_ari_cases__54 Symptoms Acute respiratory infections Weekly -4043 Weekly reported cases of acute respiratory infections Acute respiratory illnesses are defined by the WHO as sudden/acute onset of ≥1 of the following symptoms: cough, sore throat, shortness of breath, rhinitis; and were judged by a clinician to be due to an infection. This can include diseases other than influenza, such as COVID-19. LineChart grapher__who__latest__flu__flu_monthly__reported_ari_cases__111 Symptoms Acute respiratory infections Monthly -4043 Monthly reported cases of acute respiratory infections Acute respiratory illnesses are defined by the WHO as sudden/acute onset of ≥1 of the following symptoms: cough, sore throat, shortness of breath, rhinitis; and were judged by a clinician to be due to an infection. This can include diseases other than influenza, such as COVID-19. LineChart grapher__who__latest__flu__flu__reported_sari_cases__56 Symptoms Severe acute respiratory infections Weekly -4043 Weekly reported cases of severe acute respiratory infections Severe acute respiratory illnesses are defined by the WHO as acute respiratory infections with history of fever or measured fever of ≥ 38 C°, cough, with onset within the last 10 days, and which require hospitalization. This can include diseases other than influenza, such as COVID-19. LineChart grapher__who__latest__flu__flu_monthly__reported_sari_cases__113 Symptoms Severe acute respiratory infections Monthly -4043 Monthly reported cases of severe acute respiratory infections Severe acute respiratory illnesses are defined by the WHO as acute respiratory infections with history of fever or measured fever of ≥ 38 C°, cough, with onset within the last 10 days, and which require hospitalization. This can include diseases other than influenza, such as COVID-19. LineChart columns catalogPath slug transform additionalInfo color colorScaleNumericBins colorScaleNumericMinValue colorScaleScheme dataPublishedBy sourceLink sourceName tolerance type grapher__who__latest__flu__flu__pcnt_poscombined__51 duplicate 1025147 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 5;10;15;20;25;30;35;40;45;50 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Percentage grapher__who__latest__flu__flu_monthly__pcnt_poscombined__108 duplicate 1025225 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 5;10;15;20;25;30;35;40;45;50 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Percentage grapher__who__latest__flu__flu__pcnt_possentinel__53 duplicate 1025144 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 5;10;15;20;25;30;35;40;45;50 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Percentage grapher__who__latest__flu__flu_monthly__pcnt_possentinel__110 duplicate 1025222 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 5;10;15;20;25;30;35;40;45;50 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Percentage grapher__who__latest__flu__flu__pcnt_posnonsentinel__52 duplicate 1025146 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 5;10;15;20;25;30;35;40;45;50 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Percentage grapher__who__latest__flu__flu_monthly__pcnt_posnonsentinel__109 duplicate 1025224 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 5;10;15;20;25;30;35;40;45;50 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Percentage grapher__who__latest__flu__flu__ah1n12009combined_zfilled__9 duplicate 1025149 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #8C2D04 0.0 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 0.0 Integer grapher__who__latest__flu__flu__ah1combined_zfilled__7 duplicate 1025148 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #CC4C02 0.0 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 0.0 Integer grapher__who__latest__flu__flu__ah3combined_zfilled__15 duplicate 1025150 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #EC7014 0.0 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 0.0 Integer grapher__who__latest__flu__flu__ah5combined_zfilled__21 duplicate 1025151 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #FE9929 0.0 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 0.0 Integer grapher__who__latest__flu__flu__ah7n9combined_zfilled__23 duplicate 1025152 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #FEC44F 0.0 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 0.0 Integer grapher__who__latest__flu__flu__a_no_subtypecombined_zfilled__1 duplicate 1025153 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #FEE391 0.0 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 0.0 Integer grapher__who__latest__flu__flu__bviccombined_zfilled__31 duplicate 1025155 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #02818A 0.0 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 0.0 Integer grapher__who__latest__flu__flu__byamcombined_zfilled__37 duplicate 1025154 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #67A9CF 0.0 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 0.0 Integer grapher__who__latest__flu__flu__bnotdeterminedcombined_zfilled__25 duplicate 1025156 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #BDC9E1 0.0 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 0.0 Integer grapher__who__latest__flu__flu_monthly__ah1n12009combined_zfilled__66 duplicate 1025201 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #8C2D04 0.0 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) Integer grapher__who__latest__flu__flu_monthly__ah1combined_zfilled__64 duplicate 1025203 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #CC4C02 0.0 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) Integer grapher__who__latest__flu__flu_monthly__ah3combined_zfilled__72 duplicate 1025204 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #EC7014 0.0 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) Integer grapher__who__latest__flu__flu_monthly__ah5combined_zfilled__78 duplicate 1025205 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #FE9929 0.0 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) Integer grapher__who__latest__flu__flu_monthly__ah7n9combined_zfilled__80 duplicate 1025206 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #FEC44F 0.0 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) Integer grapher__who__latest__flu__flu_monthly__a_no_subtypecombined_zfilled__58 duplicate 1025207 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #FEE391 0.0 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) Integer grapher__who__latest__flu__flu_monthly__bviccombined_zfilled__88 duplicate 1025209 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #02818A 0.0 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) Integer grapher__who__latest__flu__flu_monthly__byamcombined_zfilled__94 duplicate 1025208 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #67A9CF 0.0 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) Integer grapher__who__latest__flu__flu_monthly__bnotdeterminedcombined_zfilled__82 duplicate 1025210 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #BDC9E1 0.0 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) Integer grapher__who__latest__flu__flu__ah1n12009sentinel_zfilled__13 duplicate 1025157 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #8C2D04 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 0.0 Integer grapher__who__latest__flu__flu__ah3sentinel_zfilled__19 duplicate 1025158 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #EC7014 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 0.0 Integer grapher__who__latest__flu__flu__a_no_subtypesentinel_zfilled__5 duplicate 1025159 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #FEE391 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 0.0 Integer grapher__who__latest__flu__flu__bvicsentinel_zfilled__35 duplicate 1025162 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #02818A Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 0.0 Integer grapher__who__latest__flu__flu__byamsentinel_zfilled__41 duplicate 1025160 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #67A9CF Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 0.0 Integer grapher__who__latest__flu__flu__bnotdeterminedsentinel_zfilled__29 duplicate 1025161 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #BDC9E1 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 0.0 Integer grapher__who__latest__flu__flu_monthly__ah1n12009sentinel_zfilled__70 duplicate 1025211 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #8C2D04 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) Integer grapher__who__latest__flu__flu_monthly__ah3sentinel_zfilled__76 duplicate 1025212 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #EC7014 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) Integer grapher__who__latest__flu__flu_monthly__a_no_subtypesentinel_zfilled__62 duplicate 1025213 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #FEE391 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) Integer grapher__who__latest__flu__flu_monthly__bvicsentinel_zfilled__92 duplicate 1025216 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #02818A Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) Integer grapher__who__latest__flu__flu_monthly__byamsentinel_zfilled__98 duplicate 1025214 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #67A9CF Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) Integer grapher__who__latest__flu__flu_monthly__bnotdeterminedsentinel_zfilled__86 duplicate 1025215 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #BDC9E1 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) Integer grapher__who__latest__flu__flu__ah1n12009nonsentinel_zfilled__11 duplicate 1025163 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #8C2D04 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 0.0 Integer grapher__who__latest__flu__flu__ah3nonsentinel_zfilled__17 duplicate 1025164 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #EC7014 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 0.0 Integer grapher__who__latest__flu__flu__a_no_subtypenonsentinel_zfilled__3 duplicate 1025165 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #FEE391 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 0.0 Integer grapher__who__latest__flu__flu__bvicnonsentinel_zfilled__33 duplicate 1025168 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #02818A Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 0.0 Integer grapher__who__latest__flu__flu__byamnonsentinel_zfilled__39 duplicate 1025166 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #67A9CF Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 0.0 Integer grapher__who__latest__flu__flu__bnotdeterminednonsentinel_zfilled__27 duplicate 1025167 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #BDC9E1 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 0.0 Integer grapher__who__latest__flu__flu_monthly__ah1n12009nonsentinel_zfilled__68 duplicate 1025218 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #8C2D04 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) Integer grapher__who__latest__flu__flu_monthly__ah3nonsentinel_zfilled__74 duplicate 1025217 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #EC7014 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) Integer grapher__who__latest__flu__flu_monthly__a_no_subtypenonsentinel_zfilled__60 duplicate 1025219 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #FEE391 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) Integer grapher__who__latest__flu__flu_monthly__bvicnonsentinel_zfilled__90 duplicate 1025223 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #02818A Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) Integer grapher__who__latest__flu__flu_monthly__byamnonsentinel_zfilled__96 duplicate 1025220 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #67A9CF Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) Integer grapher__who__latest__flu__flu_monthly__bnotdeterminednonsentinel_zfilled__84 duplicate 1025221 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. #BDC9E1 Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) Integer grapher__who__latest__flu__flu__inf_allcombined__43 duplicate 1025145 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 10;50;100;500;1000;5000;10000 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__inf_allsentinel__45 duplicate 1025132 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 10;50;100;500;1000;5000 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__inf_allnonsentinel__44 duplicate 1025131 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 10;50;100;500;1000;5000 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__inf_allcombined__100 duplicate 1025202 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__inf_allsentinel__102 duplicate 1025189 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__inf_allnonsentinel__101 duplicate 1025188 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__reported_ari_cases__54 duplicate 1025112 **Dataset Description**:\n- FluID is a global human influenza surveillance dataset from the WHO that aggregates weekly data. Data comes from national influenza surveillance and regional networks such as EUROFlu.\n- The WHO defines acute respiratory illnesses (ARIs) as sudden/acute onset of ≥1 of the following symptoms: cough, sore throat, shortness of breath, rhinitis, and were judged by a clinician to be due to an infection.\n- The WHO defines influenza-like illnesses (ILIs) as acute respiratory infections with a fever ≥38ºC, a cough, and onset of symptoms within the last ten days.\n- The WHO defines severe acute respiratory infections (SARIs) as acute respiratory infections with a history of fever or measured fever of ≥38 C°, cough, with onset within the last ten days, requiring hospitalization.\n- Some countries use older definitions of these conditions.\n- Surveillance data from each country may come from sentinel sites, non-sentinel sites, or universal testing. However, the FluID dataset does not list the data under these separate categories, so we show them together.\n- You can find more detail on the surveillance strategies, sampling methods, and definitions used by each country here: https://apps.who.int/iris/bitstream/handle/10665/352183/WHO-EURO-2022-4760-44523-63025-eng.pdf?sequence=1&isAllowed=y\n\n**Data preparation steps**:\n- We remove values where the number of SARI cases is below the number of inpatients - by definition SARI cases should also be inpatients so there should always be more inpatients than SARI cases.\n- We remove values where the number of ILIs or ARIs per 1000 outpatients is ≥ 1000.\n- We remove values where the number of SARIs per 100 inpatients is ≥ 100.\n- We remove all values for the time-series of ILIs or ARIs per 1000 outpatients where all values are ≤ 1 or ≥ 999.\n- We remove all values for the time-series of SARIs per 100 inpatients where all values are ≤ 1 or ≥ 99.\n- We calculate regional aggregates by summing count variables.\n- We calculate monthly variables from weekly variables - all weeks commencing in a particular month are aggregated to that month.\n- We remove all values for years with less than 10 data points.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FID?&$format=csv FluID by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__reported_ili_cases__55 duplicate 1025115 **Dataset Description**:\n- FluID is a global human influenza surveillance dataset from the WHO that aggregates weekly data. Data comes from national influenza surveillance and regional networks such as EUROFlu.\n- The WHO defines acute respiratory illnesses (ARIs) as sudden/acute onset of ≥1 of the following symptoms: cough, sore throat, shortness of breath, rhinitis, and were judged by a clinician to be due to an infection.\n- The WHO defines influenza-like illnesses (ILIs) as acute respiratory infections with a fever ≥38ºC, a cough, and onset of symptoms within the last ten days.\n- The WHO defines severe acute respiratory infections (SARIs) as acute respiratory infections with a history of fever or measured fever of ≥38 C°, cough, with onset within the last ten days, requiring hospitalization.\n- Some countries use older definitions of these conditions.\n- Surveillance data from each country may come from sentinel sites, non-sentinel sites, or universal testing. However, the FluID dataset does not list the data under these separate categories, so we show them together.\n- You can find more detail on the surveillance strategies, sampling methods, and definitions used by each country here: https://apps.who.int/iris/bitstream/handle/10665/352183/WHO-EURO-2022-4760-44523-63025-eng.pdf?sequence=1&isAllowed=y\n\n**Data preparation steps**:\n- We remove values where the number of SARI cases is below the number of inpatients - by definition SARI cases should also be inpatients so there should always be more inpatients than SARI cases.\n- We remove values where the number of ILIs or ARIs per 1000 outpatients is ≥ 1000.\n- We remove values where the number of SARIs per 100 inpatients is ≥ 100.\n- We remove all values for the time-series of ILIs or ARIs per 1000 outpatients where all values are ≤ 1 or ≥ 999.\n- We remove all values for the time-series of SARIs per 100 inpatients where all values are ≤ 1 or ≥ 99.\n- We calculate regional aggregates by summing count variables.\n- We calculate monthly variables from weekly variables - all weeks commencing in a particular month are aggregated to that month.\n- We remove all values for years with less than 10 data points.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FID?&$format=csv FluID by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__reported_sari_cases__56 duplicate 1025113 **Dataset Description**:\n- FluID is a global human influenza surveillance dataset from the WHO that aggregates weekly data. Data comes from national influenza surveillance and regional networks such as EUROFlu.\n- The WHO defines acute respiratory illnesses (ARIs) as sudden/acute onset of ≥1 of the following symptoms: cough, sore throat, shortness of breath, rhinitis, and were judged by a clinician to be due to an infection.\n- The WHO defines influenza-like illnesses (ILIs) as acute respiratory infections with a fever ≥38ºC, a cough, and onset of symptoms within the last ten days.\n- The WHO defines severe acute respiratory infections (SARIs) as acute respiratory infections with a history of fever or measured fever of ≥38 C°, cough, with onset within the last ten days, requiring hospitalization.\n- Some countries use older definitions of these conditions.\n- Surveillance data from each country may come from sentinel sites, non-sentinel sites, or universal testing. However, the FluID dataset does not list the data under these separate categories, so we show them together.\n- You can find more detail on the surveillance strategies, sampling methods, and definitions used by each country here: https://apps.who.int/iris/bitstream/handle/10665/352183/WHO-EURO-2022-4760-44523-63025-eng.pdf?sequence=1&isAllowed=y\n\n**Data preparation steps**:\n- We remove values where the number of SARI cases is below the number of inpatients - by definition SARI cases should also be inpatients so there should always be more inpatients than SARI cases.\n- We remove values where the number of ILIs or ARIs per 1000 outpatients is ≥ 1000.\n- We remove values where the number of SARIs per 100 inpatients is ≥ 100.\n- We remove all values for the time-series of ILIs or ARIs per 1000 outpatients where all values are ≤ 1 or ≥ 999.\n- We remove all values for the time-series of SARIs per 100 inpatients where all values are ≤ 1 or ≥ 99.\n- We calculate regional aggregates by summing count variables.\n- We calculate monthly variables from weekly variables - all weeks commencing in a particular month are aggregated to that month.\n- We remove all values for years with less than 10 data points.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FID?&$format=csv FluID by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__reported_ari_cases__111 duplicate 1025169 **Dataset Description:**\n- FluID is a global human influenza surveillance dataset from the WHO that aggregates weekly data. Data comes from national influenza surveillance and regional networks such as EUROFlu.\n- The WHO defines acute respiratory illnesses (ARIs) as sudden/acute onset of ≥1 of the following symptoms: cough, sore throat, shortness of breath, coryza, and were judged by a clinician to be due to an infection.\n- The WHO defines influenza-like illnesses (ILIs) as acute respiratory infections with a fever ≥38ºC, a cough, and onset of symptoms within the last ten days.\n- The WHO defines severe acute respiratory infections (SARIs) as acute respiratory infections with a history of fever or measured fever of ≥38 C°, cough, with onset within the last ten days, requiring hospitalization.\n- Some countries use older definitions of these conditions.\n- Surveillance data from each country may come from sentinel sites, non-sentinel sites, or universal testing. However, the FluID dataset does not list the data under these separate categories, so we show them together.\n- You can find more detail on the surveillance strategies, sampling methods, and definitions used by each country here: https://apps.who.int/iris/bitstream/handle/10665/352183/WHO-EURO-2022-4760-44523-63025-eng.pdf?sequence=1&isAllowed=y\n\n**Data preparation steps:**\n- We remove values where the number of SARI cases is below the number of inpatients - by definition SARI cases should also be inpatients so there should always be more inpatients than SARI cases.\n- We remove values where the number of ILIs or ARIs per 1000 outpatients is ≥ 1000.\n- We remove values where the number of SARIs per 100 inpatients is ≥ 100.\n- We remove all values for the time-series of ILIs or ARIs per 1000 outpatients where all values are ≤ 1 or ≥ 999.\n- We remove all values for the time-series of SARIs per 100 inpatients where all values are ≤ 1 or ≥ 99.\n- We calculate regional aggregates by summing count variables.\n- We calculate monthly variables from weekly variables - all weeks commencing in a particular month are aggregated to that month.\n- We remove all values for years with less than 10 data points.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FID?&$format=csv FluID by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__reported_ili_cases__112 duplicate 1025171 **Dataset Description:**\n- FluID is a global human influenza surveillance dataset from the WHO that aggregates weekly data. Data comes from national influenza surveillance and regional networks such as EUROFlu.\n- The WHO defines acute respiratory illnesses (ARIs) as sudden/acute onset of ≥1 of the following symptoms: cough, sore throat, shortness of breath, coryza, and were judged by a clinician to be due to an infection.\n- The WHO defines influenza-like illnesses (ILIs) as acute respiratory infections with a fever ≥38ºC, a cough, and onset of symptoms within the last ten days.\n- The WHO defines severe acute respiratory infections (SARIs) as acute respiratory infections with a history of fever or measured fever of ≥38 C°, cough, with onset within the last ten days, requiring hospitalization.\n- Some countries use older definitions of these conditions.\n- Surveillance data from each country may come from sentinel sites, non-sentinel sites, or universal testing. However, the FluID dataset does not list the data under these separate categories, so we show them together.\n- You can find more detail on the surveillance strategies, sampling methods, and definitions used by each country here: https://apps.who.int/iris/bitstream/handle/10665/352183/WHO-EURO-2022-4760-44523-63025-eng.pdf?sequence=1&isAllowed=y\n\n**Data preparation steps:**\n- We remove values where the number of SARI cases is below the number of inpatients - by definition SARI cases should also be inpatients so there should always be more inpatients than SARI cases.\n- We remove values where the number of ILIs or ARIs per 1000 outpatients is ≥ 1000.\n- We remove values where the number of SARIs per 100 inpatients is ≥ 100.\n- We remove all values for the time-series of ILIs or ARIs per 1000 outpatients where all values are ≤ 1 or ≥ 999.\n- We remove all values for the time-series of SARIs per 100 inpatients where all values are ≤ 1 or ≥ 99.\n- We calculate regional aggregates by summing count variables.\n- We calculate monthly variables from weekly variables - all weeks commencing in a particular month are aggregated to that month.\n- We remove all values for years with less than 10 data points.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FID?&$format=csv FluID by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__reported_sari_cases__113 duplicate 1025170 **Dataset Description:**\n- FluID is a global human influenza surveillance dataset from the WHO that aggregates weekly data. Data comes from national influenza surveillance and regional networks such as EUROFlu.\n- The WHO defines acute respiratory illnesses (ARIs) as sudden/acute onset of ≥1 of the following symptoms: cough, sore throat, shortness of breath, coryza, and were judged by a clinician to be due to an infection.\n- The WHO defines influenza-like illnesses (ILIs) as acute respiratory infections with a fever ≥38ºC, a cough, and onset of symptoms within the last ten days.\n- The WHO defines severe acute respiratory infections (SARIs) as acute respiratory infections with a history of fever or measured fever of ≥38 C°, cough, with onset within the last ten days, requiring hospitalization.\n- Some countries use older definitions of these conditions.\n- Surveillance data from each country may come from sentinel sites, non-sentinel sites, or universal testing. However, the FluID dataset does not list the data under these separate categories, so we show them together.\n- You can find more detail on the surveillance strategies, sampling methods, and definitions used by each country here: https://apps.who.int/iris/bitstream/handle/10665/352183/WHO-EURO-2022-4760-44523-63025-eng.pdf?sequence=1&isAllowed=y\n\n**Data preparation steps:**\n- We remove values where the number of SARI cases is below the number of inpatients - by definition SARI cases should also be inpatients so there should always be more inpatients than SARI cases.\n- We remove values where the number of ILIs or ARIs per 1000 outpatients is ≥ 1000.\n- We remove values where the number of SARIs per 100 inpatients is ≥ 100.\n- We remove all values for the time-series of ILIs or ARIs per 1000 outpatients where all values are ≤ 1 or ≥ 999.\n- We remove all values for the time-series of SARIs per 100 inpatients where all values are ≤ 1 or ≥ 99.\n- We calculate regional aggregates by summing count variables.\n- We calculate monthly variables from weekly variables - all weeks commencing in a particular month are aggregated to that month.\n- We remove all values for years with less than 10 data points.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FID?&$format=csv FluID by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__inf_asentinel__47 duplicate 1025122 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 10;50;100;500;1000 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__inf_asentinel__104 duplicate 1025180 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__inf_anonsentinel__46 duplicate 1025121 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 10;50;100;500;1000 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__inf_anonsentinel__103 duplicate 1025178 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__inf_acombined__42 duplicate 1025139 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 10;50;100;500;1000;5000 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__inf_acombined__99 duplicate 1025196 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__ah1n12009sentinel__12 duplicate 1025116 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__ah1n12009sentinel__69 duplicate 1025173 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__ah1n12009nonsentinel__10 duplicate 1025114 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__ah1n12009nonsentinel__67 duplicate 1025172 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__ah1n12009combined__8 duplicate 1025134 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__ah1n12009combined__65 duplicate 1025190 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__ah1combined__6 duplicate 1025133 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__ah1combined__63 duplicate 1025191 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__ah3sentinel__18 duplicate 1025118 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__ah3sentinel__75 duplicate 1025175 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__ah3nonsentinel__16 duplicate 1025117 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__ah3nonsentinel__73 duplicate 1025174 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__ah3combined__14 duplicate 1025135 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__ah3combined__71 duplicate 1025192 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__ah5combined__20 duplicate 1025137 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__ah7n9combined__22 duplicate 1025136 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__a_no_subtypesentinel__4 duplicate 1025120 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__a_no_subtypenonsentinel__2 duplicate 1025119 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__a_no_subtypecombined__0 duplicate 1025138 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__inf_bsentinel__50 duplicate 1025130 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 10;50;100;500 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__inf_bnonsentinel__49 duplicate 1025129 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 10;50;100;500;1000 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__inf_bcombined__48 duplicate 1025143 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 10;50;100;500;1000 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__bvicsentinel__34 duplicate 1025128 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__bvicnonsentinel__32 duplicate 1025127 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__bviccombined__30 duplicate 1025142 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__byamsentinel__40 duplicate 1025124 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__byamnonsentinel__38 duplicate 1025123 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__byamcombined__36 duplicate 1025140 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__bnotdeterminedsentinel__28 duplicate 1025126 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__bnotdeterminednonsentinel__26 duplicate 1025125 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu__bnotdeterminedcombined__24 duplicate 1025141 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n- Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__ah5combined__77 duplicate 1025194 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 1;2;5;10; 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__ah7n9combined__79 duplicate 1025193 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__a_no_subtypesentinel__61 duplicate 1025177 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__a_no_subtypenonsentinel__59 duplicate 1025176 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__a_no_subtypecombined__57 duplicate 1025195 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__inf_bsentinel__107 duplicate 1025187 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__inf_bnonsentinel__106 duplicate 1025186 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__inf_bcombined__105 duplicate 1025200 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__byamsentinel__97 duplicate 1025181 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__byamnonsentinel__95 duplicate 1025179 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__byamcombined__93 duplicate 1025197 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__bvicsentinel__91 duplicate 1025185 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__bvicnonsentinel__89 duplicate 1025183 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__bviccombined__87 duplicate 1025199 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__bnotdeterminedsentinel__85 duplicate 1025184 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__bnotdeterminednonsentinel__83 duplicate 1025182 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer grapher__who__latest__flu__flu_monthly__bnotdeterminedcombined__81 duplicate 1025198 **Dataset Description:**\n- FluNET is a human influenza surveillance dataset that aggregates data from (1) the Global Influenza Surveillance and Response System (GISRS), (2) other national influenza reference laboratories that collaborate with the GISRS, and (3) other influenza surveillance data uploaded from WHO regional databases.\n-Some of these samples are tested to determine whether they are influenza and whether they are influenza A or influenza B. Some surveillance centers also test the samples to identify their subtype. These are described as strains for influenza A (e.g., A H7N9) and lineages for influenza B (e.g., B Yamagata). This testing can use molecular detection, virus culture, or immunological methods.\n\n**Surveillance types:**\n- Surveillance data from each country may come from sentinel sites or non-sentinel sites.\n- Sentinel sites are health centers in a country that are selected to perform high-quality testing of cases: they test for flu subtypes and lineages in a routine and timely manner.\n- They are also selected in a way that is aimed to be representative of the population. For example, they include health centers in both urban and rural areas, are general hospitals rather than specialist centers, and cater to a wide range of demographics.\n- The WHO provides guidelines to countries for selecting centers which will be sentinel sites, using these criteria. The selection also depends on which centers have the resources for data collection, testing and reporting.\n- Sentinel data can give a representative picture of trends across time in the country. But, since not all health centers and clinics are included, the data does not tell us about the total number of infections across the country. See also: World Health Organization. (1999). WHO Recommended Surveillance Standards. Second edition. https://web.archive.org/web/20220121021824/http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf\n- Data on flu can also come from 'non-sentinel' sites. This includes other kinds of testing, which may be done at point of care, in universal testing, or during outbreak investigation.\n-Some samples are not categorized as either sentinel or non-sentinel surveillance. These are included in the data for 'all types' of surveillance.\n\n**Data preparation steps:**\n- Remove rows where confirmed cases by influenza strain do not sum as expected. Specifically:\n· Where all the influenza A strains do not sum to the total value given for influenza A\n· Where all the influenza B strains do not sum to the total value given for influenza B\n· Where influenza A + influenza B does not equal total influenza confirmed cases\n\n- We follow the WHO’s method to calculate the ‘Share of tests that test positive (%).’ They suggest using three possible denominators, in order of preference:\n· Number of negative influenza tests + number of positive tests\n· Number of specimens processed\n· Number of specimens received\n- We follow the above approach at the country level. At the regional level, we do not consider the number of negative influenza tests as, historically, there is not much data available for this variable.\n· For example, there are many weeks where only one country has a value for negative influenza tests. Instead, we use specimens processed as the primary denominator as data for this is much more plentiful.\n\n- We set the negative tests to NA where the sum of negative tests is 0, and the sum of the negative and positive tests does not equal the number of processed tests.\n- We calculate an aggregate for the UK by summing data for England, N.Ireland, Scotland, and Wales.\n- We calculate global and hemisphere aggregates by summing count variables and averaging rate variables.\n- We calculate monthly from weekly variables by aggregating all weeks commencing in a particular month to that month.\n- We remove all values for years with less than 10 data points.\n- We remove all values for time series where the only values are 0 or NA.\n- We do not show data for the most recent 21 days. Data covering the most recent 21 days is often adjusted and updated in the weeks following its release. 0.0 YlOrRd Global Influenza Surveillance and Response System, World Health Organization https://frontdoor-l4uikgap6gz3m.azurefd.net/FLUMART/VIW_FNT?&$format=csv FluNet by the World Health Organization (2023) 30.0 Integer | True |