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| 32535 | Mortality Risk of COVID-19 | mortality-risk-covid | page | publish | <!-- wp:html --> <!-- formatting-options subnavId:coronavirus subnavCurrentId:mortality-risk --> <!-- /wp:html --> <!-- wp-block-tombstone 50616 --> <!-- wp:html --> <style> .alert-banner { display:none; } </style> <!-- /wp:html --> <!-- wp-block-tombstone 38962 --> <!-- wp-block-tombstone 34317 --> <!-- wp:owid/byline --> <!-- wp:paragraph --> <p><a href="https://ourworldindata.org/team"><strong>Research and data:</strong> Edouard Mathieu, Hannah Ritchie, Lucas Rodés-Guirao, Cameron Appel, Daniel Gavrilov, Charlie Giattino, Joe Hasell, Bobbie Macdonald, Saloni Dattani, Diana Beltekian, Esteban Ortiz-Ospina, and Max Roser</a></p> <!-- /wp:paragraph --> <!-- /wp:owid/byline --> <!-- wp:html --> <div class="blog-info"> <p>We are grateful to everyone whose editorial review and expert feedback on this work helps us to continuously improve our work on the pandemic. Thank you. <a href="#acknowledgements"><strong>Here</strong></a> you find the acknowledgements.</p> </div> <!-- /wp:html --> <!-- wp-block-tombstone 32069 --> <!-- wp:owid/last-updated --> <!-- wp:paragraph {"placeholder":"Enter last updated information..."} --> <p>The data on the coronavirus pandemic is updated daily. {{LastUpdated timestampUrl:https://covid.ourworldindata.org/data/internal/timestamp/owid-covid-data-last-updated-timestamp-root.txt}}</p> <!-- /wp:paragraph --> <!-- /wp:owid/last-updated --> <!-- wp:columns {"className":"is-style-side-by-side"} --> <div class="wp-block-columns is-style-side-by-side"><!-- wp:column --> <div class="wp-block-column"><!-- wp-block-tombstone 33512 --> </div> <!-- /wp:column --> <!-- wp:column --> <div class="wp-block-column"></div> <!-- /wp:column --></div> <!-- /wp:columns --> <!-- wp:paragraph --> <p>Our interactive data visualizations that show the case fatality rate in each country are updated daily.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>The text below is updated periodically. In the last update we replaced some of the earlier content on mortality risks by age and preexisting health conditions from before vaccines were available.</p> <!-- /wp:paragraph --> <!-- wp-block-tombstone 32975 --> <!-- wp:heading --> <h2>Case fatality rate</h2> <!-- /wp:heading --> <!-- wp:columns {"className":"is-style-sticky-left"} --> <div class="wp-block-columns is-style-sticky-left"><!-- wp:column --> <div class="wp-block-column"><!-- wp:html --> <iframe src="https://ourworldindata.org/coronavirus-data-explorer?zoomToSelection=true&time=2020-03-14..&country=OWID_WRL~USA~ITA~BRA~ESP~SWE~DEU~IND~IRN~KOR~NZL&cfrMetric=true&interval=total&aligned=true&hideControls=true&smoothing=0&pickerMetric=location&pickerSort=asc" loading="lazy" style="width: 100%; height: 600px; border: 0px none;"></iframe> <!-- /wp:html --> <!-- wp:heading {"level":5} --> <h5>Related chart:</h5> <!-- /wp:heading --> <!-- wp:owid/prominent-link {"title":"Total confirmed deaths vs. cases [scatterplot]","linkUrl":"https://ourworldindata.org/grapher/covid-19-total-confirmed-cases-vs-total-confirmed-deaths","mediaId":32490,"mediaUrl":"https://owid.cloud/app/uploads/2020/04/covid-19-total-confirmed-cases-vs-total-confirmed-deaths.svg","mediaAlt":"","className":"is-style-thin"} --> <!-- wp:paragraph --> <p>How do the total number of confirmed deaths and cases compare? See them plotted against each other.</p> <!-- /wp:paragraph --> <!-- /wp:owid/prominent-link --> <!-- wp:owid/prominent-link {"title":"Total confirmed deaths and cases [time-series]","linkUrl":"https://ourworldindata.org/grapher/cumulative-deaths-and-cases-covid-19","className":"is-style-thin"} --> <!-- wp:paragraph --> <p>How has the number of confirmed cases and confirmed deaths changed over time?</p> <!-- /wp:paragraph --> <!-- /wp:owid/prominent-link --></div> <!-- /wp:column --> <!-- wp:column --> <div class="wp-block-column"><!-- wp:paragraph --> <p>The case fatality rate (CFR) is simply the <strong>number of confirmed deaths divided by the number of confirmed cases</strong>.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>This chart here plots the CFR calculated in this way. </p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>When the number of actual cases and deaths is not known – as is the case for COVID – one has to be careful in interpreting the CFR. In the sections that follow we explain what we can and cannot learn about the mortality risk based on the CFR.</p> <!-- /wp:paragraph --></div> <!-- /wp:column --></div> <!-- /wp:columns --> <!-- wp:heading --> <h2>Understanding the case fatality, crude mortality, and the infection fatality rate</h2> <!-- /wp:heading --> <!-- wp-block-tombstone 31335 --> <!-- wp:heading {"level":4} --> <h4>Case fatality rate (CFR)</h4> <!-- /wp:heading --> <!-- wp:paragraph --> <p>In the media, it is often the "case fatality rate" that is talked about when the risk of death from COVID-19 is discussed.{ref}For examples see Worldometers. Worldometers lists many poor examples of 'mortality rates' for COVID-19 without discussion<a href="https://www.worldometers.info/coronavirus/coronavirus-death-rate/"> here</a>.{/ref} </p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>This measure is sometimes also called case fatality risk or case fatality ratio. It is often abbreviated as CFR.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>The CFR is not the same as the risk of death for an infected person – even though, unfortunately, journalists sometimes suggest that it is. It is relevant and important, but far from the whole story.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>The CFR is easy to calculate. You take the number of people who have died from the disease, and you divide it by the total number of people diagnosed with the disease. So if 10 people have died, and 100 people have been diagnosed with the disease, the CFR is [10 / 100], or 10%.</p> <!-- /wp:paragraph --> <!-- wp:shortcode --> <br>[latex]\text{ Case Fatality Rate (CFR) }=\frac{\text{ Number of deaths from disease }}{\text{ Number of diagnosed cases of disease }}\times100[/latex]<br> <!-- /wp:shortcode --> <!-- wp:paragraph --> <p>But it's important to note that it is the ratio between the number of <em>confirmed</em> deaths from the disease and the number of <em>confirmed </em>cases, not total cases. That means that it is not the same as – and, in fast-moving situations like COVID-19, probably not even very close to – the true risk for an infected person.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>Another important metric, which should not be confused with the CFR, is the crude mortality rate.</p> <!-- /wp:paragraph --> <!-- wp:heading {"level":4} --> <h4>Crude mortality rate</h4> <!-- /wp:heading --> <!-- wp:paragraph --> <p>The "crude mortality rate" is another very simple measure which, like the CFR, gives something that might <em>sound</em> like the answer to the question “if someone is infected, how likely are they to die?”.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>But, just as with CFR, it is actually very different.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>The crude mortality rate – sometimes also called the crude death rate – measures the share among the <em>entire</em> population that have died from a particular disease. It’s calculated by dividing the number of deaths from the disease by the <em>total population</em>. For instance, if there were 10 deaths in a population of 1,000, the crude mortality rate would be [10 / 1,000], or 1%.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>Unfortunately, writers sometimes confuse case fatality rates and crude death rates. A common example is the Spanish flu pandemic in 1918. One estimate for the death toll of the Spanish flu, by Johnson and Mueller (2002), is that the pandemic killed 50 million people.{ref}<em>Taubenberger, J. K., & Morens, D. M. (2006).</em><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3291398/"><em> </em><em>1918 Influenza: the mother of all pandemics</em></a><em>. Revista Biomedica, 17(1), 69-79.</em>{/ref} That would have been 2.7% of the world population at the time. This means the <em>crude</em> mortality rate was 2.7%.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>But 2.7% is often misreported as the <em>case</em> fatality rate – which is wrong, because not everyone in the world was infected with the virus that caused the Spanish flu. If the crude mortality rate really was 2.7%, then the case fatality rate was much higher – it would be the percentage of people who died <em>after being diagnosed with</em> the disease. <em>[We looked at the global death count of the Spanish flu pandemic and others</em><a href="https://ourworldindata.org/spanish-flu-largest-influenza-pandemic-in-history"><em> </em><strong><em>here</em></strong></a><em>.]</em></p> <!-- /wp:paragraph --> <!-- wp:heading {"level":4} --> <h4>Infection fatality rate (IFR)</h4> <!-- /wp:heading --> <!-- wp:paragraph --> <p>The key question for understanding the mortality risk of a disease is the following: if someone is infected with the disease how likely is it that they will die from it? The answer to that question is captured by the <em>infection fatality rate</em>, or IFR.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>The IFR is the number of deaths from a disease divided by the <em>total number of cases</em>. If 10 people die of the disease, and 500 actually have it, then the IFR is [10 / 500], or 2%.{ref}We would therefore calculate the infection fatality rate as:</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p><strong>Infection fatality risk (IFR, in %)</strong> = [Number of deaths from disease / total number of cases of disease] x 100{/ref}<sup>,</sup>{ref}Wong, J. Y., Heath Kelly, D. K., Wu, J. T., Leung, G. M., & Cowling, B. J. (2013).<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3809029/"> Case fatality risk of influenza A (H1N1pdm09): a systematic review</a>. <em>Epidemiology</em>, 24(6).{/ref}<sup>,</sup>{ref}Lipsitch, M., Donnelly, C. A., Fraser, C., Blake, I. M., Cori, A., Dorigatti, I., ... & Van Kerkhove, M. D. (2015). Potential biases in estimating absolute and relative case-fatality risks during outbreaks. <em>PLoS Neglected Tropical Diseases</em>, <em>9</em>(7).{/ref}<sup>,</sup>{ref}Kobayashi, T., Jung, S. M., Linton, N. M., Kinoshita, R., Hayashi, K., Miyama, T., ... & Suzuki, A. (2020).<a href="https://www.mdpi.com/2077-0383/9/2/580"> Communicating the Risk of Death from Novel Coronavirus Disease (COVID-19)</a>. <em>Journal of Clinical Medicine</em>.{/ref}<sup>,</sup>{ref}Nishiura, H. (2010).<a href="https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(10)70120-1/fulltext"> Case fatality ratio of pandemic influenza</a>. <em>The Lancet Infectious Diseases</em>, 10(7), 443.{/ref}</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>To work out the IFR, we need two numbers: the <em>total</em> number of cases and the <em>total</em> number of deaths from the disease. </p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>However, as <a href="https://ourworldindata.org/covid-cases#the-number-of-actual-cases-is-higher-than-the-number-of-confirmed-cases" target="_blank" rel="noreferrer noopener">we explain here</a>, the <em>total</em> number of cases of COVID-19 is not known. One big reason for this is that not everyone with COVID-19 is tested.{ref}Read JM, Bridgen JR, Cummings DA, Ho A, Jewell CP.<a href="https://www.medrxiv.org/content/10.1101/2020.01.23.20018549v2"> Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions</a>. medRxiv. 2020;2020.01.23.20018549.{/ref}<sup>,</sup>{ref}World Health Organization (2020). Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Available online at: https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf{/ref} </p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>The total number of cases is not known, so the IFR cannot be simply calculated from observed data. But, researchers are able to <em>estimate</em> the total number of cases and use that to calculate the IFR – we get to this further below.</p> <!-- /wp:paragraph --> <!-- wp:heading {"level":4} --> <h4>Why the Case Fatality Rate does not reflect the risk of dying from COVID-19</h4> <!-- /wp:heading --> <!-- wp:paragraph --> <p>There is a straightforward question that most people would like answered. If someone is infected with COVID-19, how likely is that person to die? </p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>This question is simple, but surprisingly hard to answer.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>The key point is that the case fatality rate (CFR) – the most commonly discussed measure – is not the answer to the question. </p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>The main reason why it does not answer that question is that the CFR relies on the number of <em>confirmed</em> cases, and many cases are not confirmed.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>In order to understand what the case fatality rate can and cannot tell us about a disease outbreak such as COVID-19, it's important to understand why it is difficult to measure and interpret the numbers.</p> <!-- /wp:paragraph --> <!-- wp:heading {"level":4} --> <h4>The case fatality rate isn't constant: it changes with the context</h4> <!-- /wp:heading --> <!-- wp:paragraph --> <p>Sometimes commentators talk about the CFR as if it's a single, steady number: an unchanging fact about the disease.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>But it's not a biological constant; instead, it reflects the situation <em>in a particular context, at a particular time, in a particular population.</em></p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>The probability that someone dies from a disease doesn't just depend on the disease itself, but also on the treatment they receive, and on the patient's own ability to recover from it.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>This means that the CFR can decrease or increase over time, as responses change; and it can vary by location and by the characteristics of the infected population, such as age, or sex. For instance, older populations would expect to see a higher CFR from COVID-19 than younger ones.</p> <!-- /wp:paragraph --> <!-- wp:heading {"level":4} --> <h4>The CFR of COVID-19 differs by location, and has changed during the early period of the outbreak</h4> <!-- /wp:heading --> <!-- wp:paragraph --> <p>The case fatality rate of COVID-19 is not constant. This was clear right from the start of the pandemic. You can see that in the chart below, first published in the <em>Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19)</em>, in February 2020.{ref}World Health Organization (2020). Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Available online at:<a href="https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf"> https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf</a>.{/ref}</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>It shows the CFR for COVID-19 in several locations in China during the early stages of the outbreak, from the beginning of January to 20th February 2020.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>You can see that in the earliest stages of the outbreak the CFR was much higher: 17.3% across China as a whole (in yellow) and greater than 20% in the center of the outbreak, in Wuhan (in blue).</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>But in the weeks that followed, the CFR declined, reaching as low as 0.7% for patients who first showed symptoms after February 1st. The WHO reported that that was because “the standard of care has evolved over the course of the outbreak”.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>You can also see that the CFR was different in different places. By 1st February, the CFR in Wuhan was still 5.8% while it was 0.7% across the rest of China.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>This shows that what we said about the CFR generally – that it changes from time to time and place to place – is true for the CFR of COVID-19 specifically. When we talk about the CFR of a disease, we need to talk about it <em>in a specific time and place </em>– the CFR in Wuhan on 23rd February, or in Italy on 4th March – rather than as a single unchanging value.</p> <!-- /wp:paragraph --> <!-- wp:html --> <h6>Case fatality ratio for COVID-19 in China over time and by location, as of 20 February 2020 - Figure 4 in WHO (2020){ref}World Health Organization (2020). Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Available online at: https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf.{/ref}</h6> <!-- /wp:html --> <!-- wp:image {"align":"center","id":30232,"sizeSlug":"large"} --> <div class="wp-block-image"><figure class="aligncenter size-large"><img src="https://owid.cloud/app/uploads/2020/03/Covid-CFR-in-China-over-time.png" alt="" class="wp-image-30232"/></figure></div> <!-- /wp:image --> <!-- wp:heading {"level":4} --> <h4>There are two reasons why the case fatality rate does not reflect the risk of death</h4> <!-- /wp:heading --> <!-- wp:paragraph --> <p>If the case fatality rate does not tell us the risk of death for someone infected with the disease, what does it tell us? And how does the CFR compare with the actual mortality risk?</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>There are two reasons why we would expect the CFR not to represent the real risk. One of them would tend to make the CFR an overestimate – the other would tend to make it an underestimate.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p><strong>When there are people who have the disease but are not diagnosed, the CFR will </strong><strong><em>overestimate </em></strong><strong>the true risk of death. With COVID-19, we think there are many undiagnosed people.</strong></p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>As we saw above, in our discussion on the difference between total and confirmed cases (<a href="https://ourworldindata.org/coronavirus#cases-of-covid-19"><strong>here</strong></a>), we do not know the number of total cases. Not everyone is tested for COVID-19, so the total number of cases is higher than the number of confirmed cases.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>Whenever there are cases of the disease that are not counted, the probability of dying from the disease is lower than the reported case fatality rate. Remember our imaginary scenario with 10 deaths and 100 cases. The CFR in that example is 10% – but if there actually 500 cases (and we’ve simply missed 400 of them due to lack of testing), then the real risk (the IFR) is just 2%.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>Or to summarize in one sentence. If the number of total cases is higher than the number of confirmed cases, then the ratio between deaths and <em>total cases</em> is smaller than the ratio between deaths and <em>confirmed cases</em>.{ref}The relationship between CFR and IFR also depends on how large of a discrepancy there is between the number of confirmed and actual deaths, but we'd expect the magnitude of undercounting of deaths to be less than for cases.{/ref}</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>Importantly, this means that the number of tests carried out affects the CFR – you can only confirm a case by testing a patient. So when we compare the CFR between different countries, the differences do not only reflect rates of mortality, but also differences in the scale of testing efforts.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p><strong>A second consideration is especially important in the early stages of an outbreak: When some people are currently sick and will die of the disease, but have not died yet, the CFR will </strong><strong><em>underestimate</em></strong><strong> the true risk of death.</strong></p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>In ongoing outbreaks, people who are currently sick will eventually die from the disease. This means that they are currently counted as a case, but will only eventually be counted as a death too. With the COVID-19 outbreak, it can take several weeks for people to go from first symptoms to death.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>This means that the CFR in the early stages is an underestimate of what it will be when the outbreak has run its course.</p> <!-- /wp:paragraph --> <!-- wp:heading {"level":3} --> <h3>Case fatality rate of other diseases</h3> <!-- /wp:heading --> <!-- wp:paragraph --> <p>Once again, we should stress what we discussed above. One has to understand the measurement challenges and the definitions to interpret estimates of the CFR for COVID-19, particularly those relating to an ongoing outbreak.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>As comparisons, the table shows the case fatality rates for other disease outbreaks. The CFR of SARS-CoV and MERS-CoV were high: 10% and 34%, respectively.{ref}Munster, V. J., Koopmans, M., van Doremalen, N., van Riel, D., & de Wit, E. (2020). <a href="https://www.nejm.org/doi/full/10.1056/NEJMp2000929">A novel coronavirus emerging in China—key questions for impact assessment</a>. <em>New England Journal of Medicine</em>, <em>382</em>(8), 692-694.{/ref}</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>The US seasonal flu has a case fatality rate of approximately 0.1% to 0.2%.{ref}The US flu data is <a href="https://www.cdc.gov/flu/about/burden/2018-2019.html">sourced</a> from the US CDC. Here we present an upper and lower estimate for the 2018-19 flu season. These two figures reflect whether we look at the percentage of deaths out of the number of symptomatic illnesses (giving us 0.1%), or the number of medical visits (giving us 0.2%). In the traditional calculation of CFR, we would tend to focus on the number of <em>symptomatic illnesses</em>. This is analogous to the number of confirmed cases, on which the COVID-19 figures are based. However, the US CDC derives these figures based on disease outbreak modelling which attempts to account for underreporting – you can read more about how it derives its annual flu figures <a href="https://www.cdc.gov/flu/about/burden/how-cdc-estimates.htm"><strong>here</strong></a>.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>This means that some of the biases which tend to underestimate the actual number of cases have been corrected for. This is not the case for the COVID-19 figures, so it may be an unfair comparison.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>Looking at estimates based on the number of medical visits may discount from the US seasonal flu data many of the kind of mild cases that may have been missed in the COVID-19 confirmed cases. However, this is likely to skew the comparison slightly in the other direction: we know that not all of the confirmed cases included in COVID-19 figures were of a severity such that they would have received a medical visit in the absence of the heightened surveillance of the outbreak.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>So, here we present both figures of the US seasonal flu figures: the CFR based on symptomatic illnesses, and those based on medical visits. It's likely that the fairest comparison to COVID-19 lies somewhere between these two values.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>You can find the data for the reported cases, medical visits and deaths from the US Centers for Disease Control and Prevention (CDC) <a href="https://www.cdc.gov/flu/about/burden/2018-2019.html">here</a>. The CDC <a href="https://www.cdc.gov/flu/about/burden/2018-2019.html">reports</a> 35,520,883 symptomatic cases of influenza in the US and 34,157 deaths from the flu. To calculate the CFR based on symptomatic illnesses, we divide the number of deaths by the number of confirmed cases and find a case fatality rate of 0.1%.{ref}(0.1%=[34,157 / 35,520,883] * 100){/ref}</p> <!-- /wp:paragraph --> <!-- wp:table {"hasFixedLayout":true,"className":"is-style-regular"} --> <figure class="wp-block-table is-style-regular"><table class="has-fixed-layout"><tbody><tr><td><strong>Disease</strong></td><td><strong>Estimated case fatality rate (CFR)</strong></td></tr><tr><td>SARS-CoV</td><td>10%<br>Venkatesh and Memish (2004)<br>Munster et al. (2020)</td></tr><tr><td>MERS-CoV</td><td>34%<br>Munster et al. (2020)</td></tr><tr><td>Seasonal flu (US)</td><td>0.1 to 0.2% <br>US CDC</td></tr><tr><td>Ebola</td><td>50%<br>40% in the 2013-16 outbreak<p>WHO (2020)<br>Shultz et al. (2016)</p> </td></tr></tbody></table></figure> <!-- /wp:table --> <!-- wp:paragraph {"style":{"typography":{"fontSize":11}}} --> <p style="font-size:11px"><strong>Sources of data shown in the table:</strong><br><strong>SARS-CoV: </strong>Venkatesh, S. & Memish, Z.A. (2004). <a href="https://apps.who.int/iris/handle/10665/119464">SARS: the new challenge to international health and travel medicine</a>. <em>EMHJ - Eastern Mediterranean Health Journal, 10 (4-5), 655-662, 2004.</em><br><strong>SARS-CoV and MERS-CoV:</strong> Munster, V. J., Koopmans, M., van Doremalen, N., van Riel, D., & de Wit, E. (2020). <a href="https://www.nejm.org/doi/full/10.1056/NEJMp2000929">A novel coronavirus emerging in China—key questions for impact assessment</a>. <em>New England Journal of Medicine</em>, <em>382</em>(8), 692-694.<br><strong>Seasonal flu:</strong> US Centers for Disease Control and Prevention (CDC). <a href="https://www.cdc.gov/flu/about/burden/2018-2019.html">Influenza Burden, 2018-19</a>.<br><strong>Ebola:</strong> Shultz, J. M., Espinel, Z., Espinola, M., & Rechkemmer, A. (2016). <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5314891/">Distinguishing epidemiological features of the 2013–2016 West Africa Ebola virus disease outbreak</a>. <em>Disaster Health</em>, <em>3</em>(3), 78-88.<br><strong>Ebola:</strong> World Health Organization (2020). <a href="https://www.who.int/en/news-room/fact-sheets/detail/ebola-virus-disease">Ebola virus disease: Factsheet</a>.</p> <!-- /wp:paragraph --> <!-- wp:heading --> <h2>Acknowledgements</h2> <!-- /wp:heading --> <!-- wp-block-tombstone 32727 --> <!-- wp:paragraph --> <p>We would like to acknowledge and thank a number of people in the development of this work: <strong>Carl Bergstrom</strong>, <strong>Bernadeta Dadonaite</strong>, <strong>Natalie Dean</strong>, <strong>Joel Hellewell,</strong> <strong>Jason Hendry</strong>, <strong>Adam Kucharski</strong>, <strong>Moritz Kraemer</strong> and <strong>Eric Topol</strong> for their very helpful and detailed comments and suggestions on earlier versions of this work. We thank <strong>Tom Chivers</strong> for his editorial review and feedback.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>And we would like to thank the many hundreds of readers who give us feedback on this work. Your feedback is what allows us to continuously clarify and improve it. We very much appreciate you taking the time to write. We cannot respond to every message we receive, but we do read all feedback and aim to take the many helpful ideas into account.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p></p> <!-- /wp:paragraph --> | {
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"text": "We are grateful to everyone whose editorial review and expert feedback on this work helps us to continuously improve our work on the pandemic. Thank you. ",
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"text": "The data on the coronavirus pandemic is updated daily. {{LastUpdated timestampUrl:https://covid.ourworldindata.org/data/internal/timestamp/owid-covid-data-last-updated-timestamp-root.txt}}",
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"text": "The text below is updated periodically. In the last update we replaced some of the earlier content on mortality risks by age and preexisting health conditions from before vaccines were available.",
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"text": "The case fatality rate (CFR) is simply the ",
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"text": "When the number of actual cases and deaths is not known \u2013 as is the case for COVID \u2013 one has to be careful in interpreting the CFR. In the sections that follow we explain what we can and cannot learn about the mortality risk based on the CFR.",
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"text": "In the media, it is often the \"case fatality rate\" that is talked about when the risk of death from COVID-19 is discussed.{ref}For examples see Worldometers. Worldometers lists many poor examples of 'mortality rates' for COVID-19 without discussion",
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"text": "The CFR is not the same as the risk of death for an infected person \u2013 even though, unfortunately, journalists sometimes suggest that it is. It is relevant and important, but far from the whole story.",
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"text": "The CFR is easy to calculate. You take the number of people who have died from the disease, and you divide it by the total number of people diagnosed with the disease. So if 10 people have died, and 100 people have been diagnosed with the disease, the CFR is [10 / 100], or 10%.",
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"text": "Unfortunately, writers sometimes confuse case fatality rates and crude death rates. A common example is the Spanish flu pandemic in 1918. One estimate for the death toll of the Spanish flu, by Johnson and Mueller (2002), is that the pandemic killed 50 million people.{ref}",
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"text": "{/ref} That would have been 2.7% of the world population at the time. This means the ",
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"text": "The key question for understanding the mortality risk of a disease is the following: if someone is infected with the disease how likely is it that they will die from it? The answer to that question is captured by the ",
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"text": "{ref}Wong, J. Y., Heath Kelly, D. K., Wu, J. T., Leung, G. M., & Cowling, B. J. (2013).",
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"url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3809029/",
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"text": " Case fatality risk of influenza A (H1N1pdm09): a systematic review",
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"text": "{ref}Lipsitch, M., Donnelly, C. A., Fraser, C., Blake, I. M., Cori, A., Dorigatti, I., ... & Van Kerkhove, M. D. (2015). Potential biases in estimating absolute and relative case-fatality risks during outbreaks. ",
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"text": "{ref}Kobayashi, T., Jung, S. M., Linton, N. M., Kinoshita, R., Hayashi, K., Miyama, T., ... & Suzuki, A. (2020).",
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"url": "https://www.mdpi.com/2077-0383/9/2/580",
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"text": " Communicating the Risk of Death from Novel Coronavirus Disease (COVID-19)",
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"text": ". ",
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"text": "{ref}Nishiura, H. (2010).",
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"text": " Case fatality ratio of pandemic influenza",
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"text": ". ",
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"text": " number of cases of COVID-19 is not known. One big reason for this is that not everyone with COVID-19 is tested.{ref}Read JM, Bridgen JR, Cummings DA, Ho A, Jewell CP.",
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"url": "https://www.medrxiv.org/content/10.1101/2020.01.23.20018549v2",
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"text": "{ref}World Health Organization (2020). Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Available online at: https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf{/ref}\u00a0",
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"text": "The key point is that the case fatality rate (CFR) \u2013 the most commonly discussed measure \u2013 is not the answer to the question.\u00a0",
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"text": "This shows that what we said about the CFR generally \u2013 that it changes from time to time and place to place \u2013 is true for the CFR of COVID-19 specifically. When we talk about the CFR of a disease, we need to talk about it ",
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"text": "As comparisons, the table shows the case fatality rates for other disease outbreaks. The CFR of SARS-CoV and MERS-CoV were high: 10% and 34%, respectively.{ref}Munster, V. J., Koopmans, M., van Doremalen, N., van Riel, D., & de Wit, E. (2020). ",
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"text": " from the US CDC. Here we present an upper and lower estimate for the 2018-19 flu season. These two figures reflect whether we look at the percentage of deaths out of the number of symptomatic illnesses (giving us 0.1%), or the number of medical visits (giving us 0.2%). In the traditional calculation of CFR, we would tend to focus on the number of ",
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"text": "Looking at estimates based on the number of medical visits may discount from the US seasonal flu data many of the kind of mild cases that may have been missed in the COVID-19 confirmed cases. However, this is likely to skew the comparison slightly in the other direction: we know that not all of the confirmed cases included in COVID-19 figures were of a severity such that they would have received a medical visit in the absence of the heightened surveillance of the outbreak.",
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"text": "Carl Bergstrom",
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"text": "Bernadeta Dadonaite",
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"text": ", ",
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"children": [
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"text": "Natalie Dean",
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"children": [
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"text": "Joel Hellewell,",
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"children": [
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"text": "Jason Hendry",
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"children": [
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"text": "Adam Kucharski",
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"text": ", ",
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"children": [
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"text": "Moritz Kraemer",
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"text": "Eric Topol",
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"text": " for their very helpful and detailed comments and suggestions on earlier versions of this work. We thank ",
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"text": "Tom Chivers",
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"text": "And we would like to thank the many hundreds of readers who give us feedback on this work. Your feedback is what allows us to continuously clarify and improve it. We very much appreciate you taking the time to write. We cannot respond to every message we receive, but we do read all feedback and aim to take the many helpful ideas into account.",
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2020-04-29 14:54:56 | 2024-06-19 11:48:32 | [
"Max Roser",
"Hannah Ritchie",
"Esteban Ortiz-Ospina",
"Joe Hasell"
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[**Research and data:** Edouard Mathieu, Hannah Ritchie, Lucas Rodés-Guirao, Cameron Appel, Daniel Gavrilov, Charlie Giattino, Joe Hasell, Bobbie Macdonald, Saloni Dattani, Diana Beltekian, Esteban Ortiz-Ospina, and Max Roser](https://ourworldindata.org/team) We are grateful to everyone whose editorial review and expert feedback on this work helps us to continuously improve our work on the pandemic. Thank you. [**Here**](#acknowledgements) you find the acknowledgements. The data on the coronavirus pandemic is updated daily. {{LastUpdated timestampUrl:https://covid.ourworldindata.org/data/internal/timestamp/owid-covid-data-last-updated-timestamp-root.txt}} Our interactive data visualizations that show the case fatality rate in each country are updated daily. The text below is updated periodically. In the last update we replaced some of the earlier content on mortality risks by age and preexisting health conditions from before vaccines were available. ## Case fatality rate <Chart url="https://ourworldindata.org/coronavirus-data-explorer?zoomToSelection=true&time=2020-03-14..&country=OWID_WRL~USA~ITA~BRA~ESP~SWE~DEU~IND~IRN~KOR~NZL&cfrMetric=true&interval=total&aligned=true&hideControls=true&smoothing=0&pickerMetric=location&pickerSort=asc"/> ##### Related chart: ### Total confirmed deaths vs. cases [scatterplot] How do the total number of confirmed deaths and cases compare? See them plotted against each other. https://ourworldindata.org/grapher/covid-19-total-confirmed-cases-vs-total-confirmed-deaths ### Total confirmed deaths and cases [time-series] How has the number of confirmed cases and confirmed deaths changed over time? https://ourworldindata.org/grapher/cumulative-deaths-and-cases-covid-19 The case fatality rate (CFR) is simply the **number of confirmed deaths divided by the number of confirmed cases**. This chart here plots the CFR calculated in this way. When the number of actual cases and deaths is not known – as is the case for COVID – one has to be careful in interpreting the CFR. In the sections that follow we explain what we can and cannot learn about the mortality risk based on the CFR. ## Understanding the case fatality, crude mortality, and the infection fatality rate #### Case fatality rate (CFR) In the media, it is often the "case fatality rate" that is talked about when the risk of death from COVID-19 is discussed.{ref}For examples see Worldometers. Worldometers lists many poor examples of 'mortality rates' for COVID-19 without discussion[ here](https://www.worldometers.info/coronavirus/coronavirus-death-rate/).{/ref} This measure is sometimes also called case fatality risk or case fatality ratio. It is often abbreviated as CFR. The CFR is not the same as the risk of death for an infected person – even though, unfortunately, journalists sometimes suggest that it is. It is relevant and important, but far from the whole story. The CFR is easy to calculate. You take the number of people who have died from the disease, and you divide it by the total number of people diagnosed with the disease. So if 10 people have died, and 100 people have been diagnosed with the disease, the CFR is [10 / 100], or 10%. [latex]\text{ Case Fatality Rate (CFR) }=\frac{\text{ Number of deaths from disease }}{\text{ Number of diagnosed cases of disease }}\times100[/latex] But it's important to note that it is the ratio between the number of _confirmed_ deaths from the disease and the number of _confirmed _cases, not total cases. That means that it is not the same as – and, in fast-moving situations like COVID-19, probably not even very close to – the true risk for an infected person. Another important metric, which should not be confused with the CFR, is the crude mortality rate. #### Crude mortality rate The "crude mortality rate" is another very simple measure which, like the CFR, gives something that might _sound_ like the answer to the question “if someone is infected, how likely are they to die?”. But, just as with CFR, it is actually very different. The crude mortality rate – sometimes also called the crude death rate – measures the share among the _entire_ population that have died from a particular disease. It’s calculated by dividing the number of deaths from the disease by the _total population_. For instance, if there were 10 deaths in a population of 1,000, the crude mortality rate would be [10 / 1,000], or 1%. Unfortunately, writers sometimes confuse case fatality rates and crude death rates. A common example is the Spanish flu pandemic in 1918. One estimate for the death toll of the Spanish flu, by Johnson and Mueller (2002), is that the pandemic killed 50 million people.{ref}_Taubenberger, J. K., & Morens, D. M. (2006)._[_ __1918 Influenza: the mother of all pandemics_](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3291398/)_. Revista Biomedica, 17(1), 69-79._{/ref} That would have been 2.7% of the world population at the time. This means the _crude_ mortality rate was 2.7%. But 2.7% is often misreported as the _case_ fatality rate – which is wrong, because not everyone in the world was infected with the virus that caused the Spanish flu. If the crude mortality rate really was 2.7%, then the case fatality rate was much higher – it would be the percentage of people who died _after being diagnosed with_ the disease. _[We looked at the global death count of the Spanish flu pandemic and others_[_ _**_here_**](https://ourworldindata.org/spanish-flu-largest-influenza-pandemic-in-history)_.]_ #### Infection fatality rate (IFR) The key question for understanding the mortality risk of a disease is the following: if someone is infected with the disease how likely is it that they will die from it? The answer to that question is captured by the _infection fatality rate_, or IFR. The IFR is the number of deaths from a disease divided by the _total number of cases_. If 10 people die of the disease, and 500 actually have it, then the IFR is [10 / 500], or 2%.{ref}We would therefore calculate the infection fatality rate as: **Infection fatality risk (IFR, in %)** = [Number of deaths from disease / total number of cases of disease] x 100{/ref},{ref}Wong, J. Y., Heath Kelly, D. K., Wu, J. T., Leung, G. M., & Cowling, B. J. (2013).[ Case fatality risk of influenza A (H1N1pdm09): a systematic review](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3809029/). _Epidemiology_, 24(6).{/ref},{ref}Lipsitch, M., Donnelly, C. A., Fraser, C., Blake, I. M., Cori, A., Dorigatti, I., ... & Van Kerkhove, M. D. (2015). Potential biases in estimating absolute and relative case-fatality risks during outbreaks. _PLoS Neglected Tropical Diseases_, _9_(7).{/ref},{ref}Kobayashi, T., Jung, S. M., Linton, N. M., Kinoshita, R., Hayashi, K., Miyama, T., ... & Suzuki, A. (2020).[ Communicating the Risk of Death from Novel Coronavirus Disease (COVID-19)](https://www.mdpi.com/2077-0383/9/2/580). _Journal of Clinical Medicine_.{/ref},{ref}Nishiura, H. (2010).[ Case fatality ratio of pandemic influenza](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(10)70120-1/fulltext). _The Lancet Infectious Diseases_, 10(7), 443.{/ref} To work out the IFR, we need two numbers: the _total_ number of cases and the _total_ number of deaths from the disease. However, as [we explain here](https://ourworldindata.org/covid-cases#the-number-of-actual-cases-is-higher-than-the-number-of-confirmed-cases), the _total_ number of cases of COVID-19 is not known. One big reason for this is that not everyone with COVID-19 is tested.{ref}Read JM, Bridgen JR, Cummings DA, Ho A, Jewell CP.[ Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions](https://www.medrxiv.org/content/10.1101/2020.01.23.20018549v2). medRxiv. 2020;2020.01.23.20018549.{/ref},{ref}World Health Organization (2020). Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Available online at: https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf{/ref} The total number of cases is not known, so the IFR cannot be simply calculated from observed data. But, researchers are able to _estimate_ the total number of cases and use that to calculate the IFR – we get to this further below. #### Why the Case Fatality Rate does not reflect the risk of dying from COVID-19 There is a straightforward question that most people would like answered. If someone is infected with COVID-19, how likely is that person to die? This question is simple, but surprisingly hard to answer. The key point is that the case fatality rate (CFR) – the most commonly discussed measure – is not the answer to the question. The main reason why it does not answer that question is that the CFR relies on the number of _confirmed_ cases, and many cases are not confirmed. In order to understand what the case fatality rate can and cannot tell us about a disease outbreak such as COVID-19, it's important to understand why it is difficult to measure and interpret the numbers. #### The case fatality rate isn't constant: it changes with the context Sometimes commentators talk about the CFR as if it's a single, steady number: an unchanging fact about the disease. But it's not a biological constant; instead, it reflects the situation _in a particular context, at a particular time, in a particular population._ The probability that someone dies from a disease doesn't just depend on the disease itself, but also on the treatment they receive, and on the patient's own ability to recover from it. This means that the CFR can decrease or increase over time, as responses change; and it can vary by location and by the characteristics of the infected population, such as age, or sex. For instance, older populations would expect to see a higher CFR from COVID-19 than younger ones. #### The CFR of COVID-19 differs by location, and has changed during the early period of the outbreak The case fatality rate of COVID-19 is not constant. This was clear right from the start of the pandemic. You can see that in the chart below, first published in the _Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19)_, in February 2020.{ref}World Health Organization (2020). Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Available online at:[ https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf](https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf).{/ref} It shows the CFR for COVID-19 in several locations in China during the early stages of the outbreak, from the beginning of January to 20th February 2020. You can see that in the earliest stages of the outbreak the CFR was much higher: 17.3% across China as a whole (in yellow) and greater than 20% in the center of the outbreak, in Wuhan (in blue). But in the weeks that followed, the CFR declined, reaching as low as 0.7% for patients who first showed symptoms after February 1st. The WHO reported that that was because “the standard of care has evolved over the course of the outbreak”. You can also see that the CFR was different in different places. By 1st February, the CFR in Wuhan was still 5.8% while it was 0.7% across the rest of China. This shows that what we said about the CFR generally – that it changes from time to time and place to place – is true for the CFR of COVID-19 specifically. When we talk about the CFR of a disease, we need to talk about it _in a specific time and place _– the CFR in Wuhan on 23rd February, or in Italy on 4th March – rather than as a single unchanging value. ###### Case fatality ratio for COVID-19 in China over time and by location, as of 20 February 2020 - Figure 4 in WHO (2020){ref}World Health Organization (2020). Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Available online at: https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf.{/ref} <Image filename="Covid-CFR-in-China-over-time.png" alt=""/> #### There are two reasons why the case fatality rate does not reflect the risk of death If the case fatality rate does not tell us the risk of death for someone infected with the disease, what does it tell us? And how does the CFR compare with the actual mortality risk? There are two reasons why we would expect the CFR not to represent the real risk. One of them would tend to make the CFR an overestimate – the other would tend to make it an underestimate. **When there are people who have the disease but are not diagnosed, the CFR will ****_overestimate _****the true risk of death. With COVID-19, we think there are many undiagnosed people.** As we saw above, in our discussion on the difference between total and confirmed cases ([**here**](https://ourworldindata.org/coronavirus#cases-of-covid-19)), we do not know the number of total cases. Not everyone is tested for COVID-19, so the total number of cases is higher than the number of confirmed cases. Whenever there are cases of the disease that are not counted, the probability of dying from the disease is lower than the reported case fatality rate. Remember our imaginary scenario with 10 deaths and 100 cases. The CFR in that example is 10% – but if there actually 500 cases (and we’ve simply missed 400 of them due to lack of testing), then the real risk (the IFR) is just 2%. Or to summarize in one sentence. If the number of total cases is higher than the number of confirmed cases, then the ratio between deaths and _total cases_ is smaller than the ratio between deaths and _confirmed cases_.{ref}The relationship between CFR and IFR also depends on how large of a discrepancy there is between the number of confirmed and actual deaths, but we'd expect the magnitude of undercounting of deaths to be less than for cases.{/ref} Importantly, this means that the number of tests carried out affects the CFR – you can only confirm a case by testing a patient. So when we compare the CFR between different countries, the differences do not only reflect rates of mortality, but also differences in the scale of testing efforts. **A second consideration is especially important in the early stages of an outbreak: When some people are currently sick and will die of the disease, but have not died yet, the CFR will ****_underestimate_**** the true risk of death.** In ongoing outbreaks, people who are currently sick will eventually die from the disease. This means that they are currently counted as a case, but will only eventually be counted as a death too. With the COVID-19 outbreak, it can take several weeks for people to go from first symptoms to death. This means that the CFR in the early stages is an underestimate of what it will be when the outbreak has run its course. ### Case fatality rate of other diseases Once again, we should stress what we discussed above. One has to understand the measurement challenges and the definitions to interpret estimates of the CFR for COVID-19, particularly those relating to an ongoing outbreak. As comparisons, the table shows the case fatality rates for other disease outbreaks. The CFR of SARS-CoV and MERS-CoV were high: 10% and 34%, respectively.{ref}Munster, V. J., Koopmans, M., van Doremalen, N., van Riel, D., & de Wit, E. (2020). [A novel coronavirus emerging in China—key questions for impact assessment](https://www.nejm.org/doi/full/10.1056/NEJMp2000929). _New England Journal of Medicine_, _382_(8), 692-694.{/ref} The US seasonal flu has a case fatality rate of approximately 0.1% to 0.2%.{ref}The US flu data is [sourced](https://www.cdc.gov/flu/about/burden/2018-2019.html) from the US CDC. Here we present an upper and lower estimate for the 2018-19 flu season. These two figures reflect whether we look at the percentage of deaths out of the number of symptomatic illnesses (giving us 0.1%), or the number of medical visits (giving us 0.2%). In the traditional calculation of CFR, we would tend to focus on the number of _symptomatic illnesses_. This is analogous to the number of confirmed cases, on which the COVID-19 figures are based. However, the US CDC derives these figures based on disease outbreak modelling which attempts to account for underreporting – you can read more about how it derives its annual flu figures [**here**](https://www.cdc.gov/flu/about/burden/how-cdc-estimates.htm). This means that some of the biases which tend to underestimate the actual number of cases have been corrected for. This is not the case for the COVID-19 figures, so it may be an unfair comparison. Looking at estimates based on the number of medical visits may discount from the US seasonal flu data many of the kind of mild cases that may have been missed in the COVID-19 confirmed cases. However, this is likely to skew the comparison slightly in the other direction: we know that not all of the confirmed cases included in COVID-19 figures were of a severity such that they would have received a medical visit in the absence of the heightened surveillance of the outbreak. So, here we present both figures of the US seasonal flu figures: the CFR based on symptomatic illnesses, and those based on medical visits. It's likely that the fairest comparison to COVID-19 lies somewhere between these two values. You can find the data for the reported cases, medical visits and deaths from the US Centers for Disease Control and Prevention (CDC) [here](https://www.cdc.gov/flu/about/burden/2018-2019.html). The CDC [reports](https://www.cdc.gov/flu/about/burden/2018-2019.html) 35,520,883 symptomatic cases of influenza in the US and 34,157 deaths from the flu. To calculate the CFR based on symptomatic illnesses, we divide the number of deaths by the number of confirmed cases and find a case fatality rate of 0.1%.{ref}(0.1%=[34,157 / 35,520,883] * 100){/ref} <div class="raw-html-table__container"><table class="has-fixed-layout"><tbody><tr><td><strong>Disease</strong></td><td><strong>Estimated case fatality rate (CFR)</strong></td></tr><tr><td>SARS-CoV</td><td>10%<br>Venkatesh and Memish (2004)<br>Munster et al. (2020)</td></tr><tr><td>MERS-CoV</td><td>34%<br>Munster et al. (2020)</td></tr><tr><td>Seasonal flu (US)</td><td>0.1 to 0.2% <br>US CDC</td></tr><tr><td>Ebola</td><td>50%<br>40% in the 2013-16 outbreak<p>WHO (2020)<br>Shultz et al. (2016)</p> </td></tr></tbody></table></div> **Sources of data shown in the table:** **SARS-CoV: **Venkatesh, S. & Memish, Z.A. (2004). [SARS: the new challenge to international health and travel medicine](https://apps.who.int/iris/handle/10665/119464). _EMHJ - Eastern Mediterranean Health Journal, 10 (4-5), 655-662, 2004._ **SARS-CoV and MERS-CoV:** Munster, V. J., Koopmans, M., van Doremalen, N., van Riel, D., & de Wit, E. (2020). [A novel coronavirus emerging in China—key questions for impact assessment](https://www.nejm.org/doi/full/10.1056/NEJMp2000929). _New England Journal of Medicine_, _382_(8), 692-694. **Seasonal flu:** US Centers for Disease Control and Prevention (CDC). [Influenza Burden, 2018-19](https://www.cdc.gov/flu/about/burden/2018-2019.html). **Ebola:** Shultz, J. M., Espinel, Z., Espinola, M., & Rechkemmer, A. (2016). [Distinguishing epidemiological features of the 2013–2016 West Africa Ebola virus disease outbreak](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5314891/). _Disaster Health_, _3_(3), 78-88. **Ebola:** World Health Organization (2020). [Ebola virus disease: Factsheet](https://www.who.int/en/news-room/fact-sheets/detail/ebola-virus-disease). ## Acknowledgements We would like to acknowledge and thank a number of people in the development of this work: **Carl Bergstrom**, **Bernadeta Dadonaite**, **Natalie Dean**, **Joel Hellewell,****Jason Hendry**, **Adam Kucharski**, **Moritz Kraemer** and **Eric Topol** for their very helpful and detailed comments and suggestions on earlier versions of this work. We thank **Tom Chivers** for his editorial review and feedback. And we would like to thank the many hundreds of readers who give us feedback on this work. Your feedback is what allows us to continuously clarify and improve it. We very much appreciate you taking the time to write. We cannot respond to every message we receive, but we do read all feedback and aim to take the many helpful ideas into account. | {
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"rendered": "\n<!-- formatting-options subnavId:coronavirus subnavCurrentId:mortality-risk -->\n\n\n\n<style>\n.alert-banner {\n display:none;\n}\n</style>\n\n\n\n\n\t<div class=\"wp-block-owid-byline\">\n\t\t\n\n<p><a href=\"https://ourworldindata.org/team\"><strong>Research and data:</strong> Edouard Mathieu, Hannah Ritchie, Lucas Rod\u00e9s-Guirao, Cameron Appel, Daniel Gavrilov, Charlie Giattino, Joe Hasell, Bobbie Macdonald, Saloni Dattani, Diana Beltekian, Esteban Ortiz-Ospina, and Max Roser</a></p>\n\n\n\t</div>\n\n\n<div class=\"blog-info\">\n<p>We are grateful to everyone whose editorial review and expert feedback on this work helps us to continuously improve our work on the pandemic. Thank you. <a href=\"#acknowledgements\"><strong>Here</strong></a> you find the acknowledgements.</p>\n</div>\n\n\n\t<div class=\"wp-block-last-updated\">\n\t\t\n\n<p>The data on the coronavirus pandemic is updated daily. {{LastUpdated timestampUrl:https://covid.ourworldindata.org/data/internal/timestamp/owid-covid-data-last-updated-timestamp-root.txt}}</p>\n\n\n\t</div>\n\n\n<div class=\"wp-block-columns is-style-side-by-side\">\n<div class=\"wp-block-column\"></div>\n\n\n\n<div class=\"wp-block-column\"></div>\n</div>\n\n\n\n<p>Our interactive data visualizations that show the case fatality rate in each country are updated daily.</p>\n\n\n\n<p>The text below is updated periodically. In the last update we replaced some of the earlier content on mortality risks by age and preexisting health conditions from before vaccines were available.</p>\n\n\n\n<h2>Case fatality rate</h2>\n\n\n\n<div class=\"wp-block-columns is-style-sticky-left\">\n<div class=\"wp-block-column\">\n<iframe src=\"https://ourworldindata.org/coronavirus-data-explorer?zoomToSelection=true&time=2020-03-14..&country=OWID_WRL~USA~ITA~BRA~ESP~SWE~DEU~IND~IRN~KOR~NZL&cfrMetric=true&interval=total&aligned=true&hideControls=true&smoothing=0&pickerMetric=location&pickerSort=asc\" loading=\"lazy\" style=\"width: 100%; height: 600px; border: 0px none;\"></iframe>\n\n\n\n<h5>Related chart:</h5>\n\n\n <block type=\"prominent-link\" style=\"is-style-thin\">\n <link-url>https://ourworldindata.org/grapher/covid-19-total-confirmed-cases-vs-total-confirmed-deaths</link-url>\n <title>Total confirmed deaths vs. cases [scatterplot]</title>\n <content>\n\n<p>How do the total number of confirmed deaths and cases compare? See them plotted against each other.</p>\n\n</content>\n <figure><img width=\"768\" height=\"1\" src=\"https://owid.cloud/app/uploads/2020/04/covid-19-total-confirmed-cases-vs-total-confirmed-deaths.svg\" class=\"attachment-medium_large size-medium_large\" alt=\"\" loading=\"lazy\" height=\"600\" width=\"850\" /></figure>\n </block>\n\n <block type=\"prominent-link\" style=\"is-style-thin\">\n <link-url>https://ourworldindata.org/grapher/cumulative-deaths-and-cases-covid-19</link-url>\n <title>Total confirmed deaths and cases [time-series]</title>\n <content>\n\n<p>How has the number of confirmed cases and confirmed deaths changed over time?</p>\n\n</content>\n <figure></figure>\n </block></div>\n\n\n\n<div class=\"wp-block-column\">\n<p>The case fatality rate (CFR) is simply the <strong>number of confirmed deaths divided by the number of confirmed cases</strong>.</p>\n\n\n\n<p>This chart here plots the CFR calculated in this way. </p>\n\n\n\n<p>When the number of actual cases and deaths is not known \u2013 as is the case for COVID \u2013 one has to be careful in interpreting the CFR. In the sections that follow we explain what we can and cannot learn about the mortality risk based on the CFR.</p>\n</div>\n</div>\n\n\n\n<h2>Understanding the case fatality, crude mortality, and the infection fatality rate</h2>\n\n\n\n<h4>Case fatality rate (CFR)</h4>\n\n\n\n<p>In the media, it is often the “case fatality rate” that is talked about when the risk of death from COVID-19 is discussed.{ref}For examples see Worldometers. Worldometers lists many poor examples of ‘mortality rates’ for COVID-19 without discussion<a href=\"https://www.worldometers.info/coronavirus/coronavirus-death-rate/\"> here</a>.{/ref} </p>\n\n\n\n<p>This measure is sometimes also called case fatality risk or case fatality ratio. It is often abbreviated as CFR.</p>\n\n\n\n<p>The CFR is not the same as the risk of death for an infected person \u2013 even though, unfortunately, journalists sometimes suggest that it is. It is relevant and important, but far from the whole story.</p>\n\n\n\n<p>The CFR is easy to calculate. You take the number of people who have died from the disease, and you divide it by the total number of people diagnosed with the disease. So if 10 people have died, and 100 people have been diagnosed with the disease, the CFR is [10 / 100], or 10%.</p>\n\n\n<p>\n <!-- latex: \\text{ Case Fatality Rate (CFR) }=\\frac{\\text{ Number of deaths from disease }}{\\text{ Number of diagnosed cases of disease }}\\times100 -->\n <math display=\"block\" style=\"display: inline-block;\">\n <mrow>\n <mtext>Case\u00a0Fatality\u00a0Rate\u00a0(CFR)</mtext>\n <mo>=</mo>\n <mfrac>\n <mtext>\u00a0Number\u00a0of\u00a0deaths\u00a0from\u00a0disease\u00a0</mtext>\n <mtext>\u00a0Number\u00a0of\u00a0diagnosed\u00a0cases\u00a0of\u00a0disease\u00a0</mtext>\n </mfrac>\n <mo>\u00d7</mo>\n <mn>100</mn>\n </mrow>\n </math>\n \n</p>\n\n\n\n<p>But it’s important to note that it is the ratio between the number of <em>confirmed</em> deaths from the disease and the number of <em>confirmed </em>cases, not total cases. That means that it is not the same as \u2013 and, in fast-moving situations like COVID-19, probably not even very close to \u2013 the true risk for an infected person.</p>\n\n\n\n<p>Another important metric, which should not be confused with the CFR, is the crude mortality rate.</p>\n\n\n\n<h4>Crude mortality rate</h4>\n\n\n\n<p>The “crude mortality rate” is another very simple measure which, like the CFR, gives something that might <em>sound</em> like the answer to the question \u201cif someone is infected, how likely are they to die?\u201d.</p>\n\n\n\n<p>But, just as with CFR, it is actually very different.</p>\n\n\n\n<p>The crude mortality rate \u2013 sometimes also called the crude death rate \u2013 measures the share among the <em>entire</em> population that have died from a particular disease. It\u2019s calculated by dividing the number of deaths from the disease by the <em>total population</em>. For instance, if there were 10 deaths in a population of 1,000, the crude mortality rate would be [10 / 1,000], or 1%.</p>\n\n\n\n<p>Unfortunately, writers sometimes confuse case fatality rates and crude death rates. A common example is the Spanish flu pandemic in 1918. One estimate for the death toll of the Spanish flu, by Johnson and Mueller (2002), is that the pandemic killed 50 million people.{ref}<em>Taubenberger, J. K., & Morens, D. M. (2006).</em><a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3291398/\"><em> </em><em>1918 Influenza: the mother of all pandemics</em></a><em>. Revista Biomedica, 17(1), 69-79.</em>{/ref} That would have been 2.7% of the world population at the time. This means the <em>crude</em> mortality rate was 2.7%.</p>\n\n\n\n<p>But 2.7% is often misreported as the <em>case</em> fatality rate \u2013 which is wrong, because not everyone in the world was infected with the virus that caused the Spanish flu. If the crude mortality rate really was 2.7%, then the case fatality rate was much higher \u2013 it would be the percentage of people who died <em>after being diagnosed with</em> the disease. <em>[We looked at the global death count of the Spanish flu pandemic and others</em><a href=\"https://ourworldindata.org/spanish-flu-largest-influenza-pandemic-in-history\"><em> </em><strong><em>here</em></strong></a><em>.]</em></p>\n\n\n\n<h4>Infection fatality rate (IFR)</h4>\n\n\n\n<p>The key question for understanding the mortality risk of a disease is the following: if someone is infected with the disease how likely is it that they will die from it? The answer to that question is captured by the <em>infection fatality rate</em>, or IFR.</p>\n\n\n\n<p>The IFR is the number of deaths from a disease divided by the <em>total number of cases</em>. If 10 people die of the disease, and 500 actually have it, then the IFR is [10 / 500], or 2%.{ref}We would therefore calculate the infection fatality rate as:</p>\n\n\n\n<p><strong>Infection fatality risk (IFR, in %)</strong> = [Number of deaths from disease / total number of cases of disease] x 100{/ref}<sup>,</sup>{ref}Wong, J. Y., Heath Kelly, D. K., Wu, J. T., Leung, G. M., & Cowling, B. J. (2013).<a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3809029/\"> Case fatality risk of influenza A (H1N1pdm09): a systematic review</a>. <em>Epidemiology</em>, 24(6).{/ref}<sup>,</sup>{ref}Lipsitch, M., Donnelly, C. A., Fraser, C., Blake, I. M., Cori, A., Dorigatti, I., … & Van Kerkhove, M. D. (2015). Potential biases in estimating absolute and relative case-fatality risks during outbreaks. <em>PLoS Neglected Tropical Diseases</em>, <em>9</em>(7).{/ref}<sup>,</sup>{ref}Kobayashi, T., Jung, S. M., Linton, N. M., Kinoshita, R., Hayashi, K., Miyama, T., … & Suzuki, A. (2020).<a href=\"https://www.mdpi.com/2077-0383/9/2/580\"> Communicating the Risk of Death from Novel Coronavirus Disease (COVID-19)</a>. <em>Journal of Clinical Medicine</em>.{/ref}<sup>,</sup>{ref}Nishiura, H. (2010).<a href=\"https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(10)70120-1/fulltext\"> Case fatality ratio of pandemic influenza</a>. <em>The Lancet Infectious Diseases</em>, 10(7), 443.{/ref}</p>\n\n\n\n<p>To work out the IFR, we need two numbers: the <em>total</em> number of cases and the <em>total</em> number of deaths from the disease. </p>\n\n\n\n<p>However, as <a href=\"https://ourworldindata.org/covid-cases#the-number-of-actual-cases-is-higher-than-the-number-of-confirmed-cases\" target=\"_blank\" rel=\"noreferrer noopener\">we explain here</a>, the <em>total</em> number of cases of COVID-19 is not known. One big reason for this is that not everyone with COVID-19 is tested.{ref}Read JM, Bridgen JR, Cummings DA, Ho A, Jewell CP.<a href=\"https://www.medrxiv.org/content/10.1101/2020.01.23.20018549v2\"> Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions</a>. medRxiv. 2020;2020.01.23.20018549.{/ref}<sup>,</sup>{ref}World Health Organization (2020). Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Available online at: https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf{/ref} </p>\n\n\n\n<p>The total number of cases is not known, so the IFR cannot be simply calculated from observed data. But, researchers are able to <em>estimate</em> the total number of cases and use that to calculate the IFR \u2013 we get to this further below.</p>\n\n\n\n<h4>Why the Case Fatality Rate does not reflect the risk of dying from COVID-19</h4>\n\n\n\n<p>There is a straightforward question that most people would like answered. If someone is infected with COVID-19, how likely is that person to die? </p>\n\n\n\n<p>This question is simple, but surprisingly hard to answer.</p>\n\n\n\n<p>The key point is that the case fatality rate (CFR) \u2013 the most commonly discussed measure \u2013 is not the answer to the question. </p>\n\n\n\n<p>The main reason why it does not answer that question is that the CFR relies on the number of <em>confirmed</em> cases, and many cases are not confirmed.</p>\n\n\n\n<p>In order to understand what the case fatality rate can and cannot tell us about a disease outbreak such as COVID-19, it’s important to understand why it is difficult to measure and interpret the numbers.</p>\n\n\n\n<h4>The case fatality rate isn’t constant: it changes with the context</h4>\n\n\n\n<p>Sometimes commentators talk about the CFR as if it’s a single, steady number: an unchanging fact about the disease.</p>\n\n\n\n<p>But it’s not a biological constant; instead, it reflects the situation <em>in a particular context, at a particular time, in a particular population.</em></p>\n\n\n\n<p>The probability that someone dies from a disease doesn’t just depend on the disease itself, but also on the treatment they receive, and on the patient’s own ability to recover from it.</p>\n\n\n\n<p>This means that the CFR can decrease or increase over time, as responses change; and it can vary by location and by the characteristics of the infected population, such as age, or sex. For instance, older populations would expect to see a higher CFR from COVID-19 than younger ones.</p>\n\n\n\n<h4>The CFR of COVID-19 differs by location, and has changed during the early period of the outbreak</h4>\n\n\n\n<p>The case fatality rate of COVID-19 is not constant. This was clear right from the start of the pandemic. You can see that in the chart below, first published in the <em>Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19)</em>, in February 2020.{ref}World Health Organization (2020). Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Available online at:<a href=\"https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf\"> https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf</a>.{/ref}</p>\n\n\n\n<p>It shows the CFR for COVID-19 in several locations in China during the early stages of the outbreak, from the beginning of January to 20th February 2020.</p>\n\n\n\n<p>You can see that in the earliest stages of the outbreak the CFR was much higher: 17.3% across China as a whole (in yellow) and greater than 20% in the center of the outbreak, in Wuhan (in blue).</p>\n\n\n\n<p>But in the weeks that followed, the CFR declined, reaching as low as 0.7% for patients who first showed symptoms after February 1st. The WHO reported that that was because \u201cthe standard of care has evolved over the course of the outbreak\u201d.</p>\n\n\n\n<p>You can also see that the CFR was different in different places. By 1st February, the CFR in Wuhan was still 5.8% while it was 0.7% across the rest of China.</p>\n\n\n\n<p>This shows that what we said about the CFR generally \u2013 that it changes from time to time and place to place \u2013 is true for the CFR of COVID-19 specifically. When we talk about the CFR of a disease, we need to talk about it <em>in a specific time and place </em>\u2013 the CFR in Wuhan on 23rd February, or in Italy on 4th March \u2013 rather than as a single unchanging value.</p>\n\n\n\n<h6>Case fatality ratio for COVID-19 in China over time and by location, as of 20 February 2020 – Figure 4 in WHO (2020){ref}World Health Organization (2020). Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Available online at: https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf.{/ref}</h6>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter size-large\"><img loading=\"lazy\" width=\"627\" height=\"385\" src=\"https://owid.cloud/app/uploads/2020/03/Covid-CFR-in-China-over-time.png\" alt=\"\" class=\"wp-image-30232\" srcset=\"https://owid.cloud/app/uploads/2020/03/Covid-CFR-in-China-over-time.png 627w, https://owid.cloud/app/uploads/2020/03/Covid-CFR-in-China-over-time-400x246.png 400w, https://owid.cloud/app/uploads/2020/03/Covid-CFR-in-China-over-time-150x92.png 150w\" sizes=\"(max-width: 627px) 100vw, 627px\" /></figure></div>\n\n\n\n<h4>There are two reasons why the case fatality rate does not reflect the risk of death</h4>\n\n\n\n<p>If the case fatality rate does not tell us the risk of death for someone infected with the disease, what does it tell us? And how does the CFR compare with the actual mortality risk?</p>\n\n\n\n<p>There are two reasons why we would expect the CFR not to represent the real risk. One of them would tend to make the CFR an overestimate \u2013 the other would tend to make it an underestimate.</p>\n\n\n\n<p><strong>When there are people who have the disease but are not diagnosed, the CFR will </strong><strong><em>overestimate </em></strong><strong>the true risk of death. With COVID-19, we think there are many undiagnosed people.</strong></p>\n\n\n\n<p>As we saw above, in our discussion on the difference between total and confirmed cases (<a href=\"https://ourworldindata.org/coronavirus#cases-of-covid-19\"><strong>here</strong></a>), we do not know the number of total cases. Not everyone is tested for COVID-19, so the total number of cases is higher than the number of confirmed cases.</p>\n\n\n\n<p>Whenever there are cases of the disease that are not counted, the probability of dying from the disease is lower than the reported case fatality rate. Remember our imaginary scenario with 10 deaths and 100 cases. The CFR in that example is 10% \u2013 but if there actually 500 cases (and we\u2019ve simply missed 400 of them due to lack of testing), then the real risk (the IFR) is just 2%.</p>\n\n\n\n<p>Or to summarize in one sentence. If the number of total cases is higher than the number of confirmed cases, then the ratio between deaths and <em>total cases</em> is smaller than the ratio between deaths and <em>confirmed cases</em>.{ref}The relationship between CFR and IFR also depends on how large of a discrepancy there is between the number of confirmed and actual deaths, but we’d expect the magnitude of undercounting of deaths to be less than for cases.{/ref}</p>\n\n\n\n<p>Importantly, this means that the number of tests carried out affects the CFR \u2013 you can only confirm a case by testing a patient. So when we compare the CFR between different countries, the differences do not only reflect rates of mortality, but also differences in the scale of testing efforts.</p>\n\n\n\n<p><strong>A second consideration is especially important in the early stages of an outbreak: When some people are currently sick and will die of the disease, but have not died yet, the CFR will </strong><strong><em>underestimate</em></strong><strong> the true risk of death.</strong></p>\n\n\n\n<p>In ongoing outbreaks, people who are currently sick will eventually die from the disease. This means that they are currently counted as a case, but will only eventually be counted as a death too. With the COVID-19 outbreak, it can take several weeks for people to go from first symptoms to death.</p>\n\n\n\n<p>This means that the CFR in the early stages is an underestimate of what it will be when the outbreak has run its course.</p>\n\n\n\n<h3>Case fatality rate of other diseases</h3>\n\n\n\n<p>Once again, we should stress what we discussed above. One has to understand the measurement challenges and the definitions to interpret estimates of the CFR for COVID-19, particularly those relating to an ongoing outbreak.</p>\n\n\n\n<p>As comparisons, the table shows the case fatality rates for other disease outbreaks. The CFR of SARS-CoV and MERS-CoV were high: 10% and 34%, respectively.{ref}Munster, V. J., Koopmans, M., van Doremalen, N., van Riel, D., & de Wit, E. (2020). <a href=\"https://www.nejm.org/doi/full/10.1056/NEJMp2000929\">A novel coronavirus emerging in China\u2014key questions for impact assessment</a>. <em>New England Journal of Medicine</em>, <em>382</em>(8), 692-694.{/ref}</p>\n\n\n\n<p>The US seasonal flu has a case fatality rate of approximately 0.1% to 0.2%.{ref}The US flu data is <a href=\"https://www.cdc.gov/flu/about/burden/2018-2019.html\">sourced</a> from the US CDC. Here we present an upper and lower estimate for the 2018-19 flu season. These two figures reflect whether we look at the percentage of deaths out of the number of symptomatic illnesses (giving us 0.1%), or the number of medical visits (giving us 0.2%). In the traditional calculation of CFR, we would tend to focus on the number of <em>symptomatic illnesses</em>. This is analogous to the number of confirmed cases, on which the COVID-19 figures are based. However, the US CDC derives these figures based on disease outbreak modelling which attempts to account for underreporting \u2013 you can read more about how it derives its annual flu figures <a href=\"https://www.cdc.gov/flu/about/burden/how-cdc-estimates.htm\"><strong>here</strong></a>.</p>\n\n\n\n<p>This means that some of the biases which tend to underestimate the actual number of cases have been corrected for. This is not the case for the COVID-19 figures, so it may be an unfair comparison.</p>\n\n\n\n<p>Looking at estimates based on the number of medical visits may discount from the US seasonal flu data many of the kind of mild cases that may have been missed in the COVID-19 confirmed cases. However, this is likely to skew the comparison slightly in the other direction: we know that not all of the confirmed cases included in COVID-19 figures were of a severity such that they would have received a medical visit in the absence of the heightened surveillance of the outbreak.</p>\n\n\n\n<p>So, here we present both figures of the US seasonal flu figures: the CFR based on symptomatic illnesses, and those based on medical visits. It’s likely that the fairest comparison to COVID-19 lies somewhere between these two values.</p>\n\n\n\n<p>You can find the data for the reported cases, medical visits and deaths from the US Centers for Disease Control and Prevention (CDC) <a href=\"https://www.cdc.gov/flu/about/burden/2018-2019.html\">here</a>. The CDC <a href=\"https://www.cdc.gov/flu/about/burden/2018-2019.html\">reports</a> 35,520,883 symptomatic cases of influenza in the US and 34,157 deaths from the flu. To calculate the CFR based on symptomatic illnesses, we divide the number of deaths by the number of confirmed cases and find a case fatality rate of 0.1%.{ref}(0.1%=[34,157 / 35,520,883] * 100){/ref}</p>\n\n\n\n<figure class=\"wp-block-table is-style-regular\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Disease</strong></td><td><strong>Estimated case fatality rate (CFR)</strong></td></tr><tr><td>SARS-CoV</td><td>10%<br>Venkatesh and Memish (\u200e2004)\u200e<br>Munster et al. (2020)</td></tr><tr><td>MERS-CoV</td><td>34%<br>Munster et al. (2020)</td></tr><tr><td>Seasonal flu (US)</td><td>0.1 to 0.2% <br>US CDC</td></tr><tr><td>Ebola</td><td>50%<br>40% in the 2013-16 outbreak<p>WHO (2020)<br>Shultz et al. (2016)</p> </td></tr></tbody></table></figure>\n\n\n\n<p style=\"font-size:11px\"><strong>Sources of data shown in the table:</strong><br><strong>SARS-CoV: </strong>Venkatesh, S. & Memish, Z.A. (\u200e2004)\u200e. <a href=\"https://apps.who.int/iris/handle/10665/119464\">SARS: the new challenge to international health and travel medicine</a>. <em>EMHJ – Eastern Mediterranean Health Journal, 10 (\u200e4-5)\u200e, 655-662, 2004.</em><br><strong>SARS-CoV and MERS-CoV:</strong> Munster, V. J., Koopmans, M., van Doremalen, N., van Riel, D., & de Wit, E. (2020). <a href=\"https://www.nejm.org/doi/full/10.1056/NEJMp2000929\">A novel coronavirus emerging in China\u2014key questions for impact assessment</a>. <em>New England Journal of Medicine</em>, <em>382</em>(8), 692-694.<br><strong>Seasonal flu:</strong> US Centers for Disease Control and Prevention (CDC). <a href=\"https://www.cdc.gov/flu/about/burden/2018-2019.html\">Influenza Burden, 2018-19</a>.<br><strong>Ebola:</strong> Shultz, J. M., Espinel, Z., Espinola, M., & Rechkemmer, A. (2016). <a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5314891/\">Distinguishing epidemiological features of the 2013\u20132016 West Africa Ebola virus disease outbreak</a>. <em>Disaster Health</em>, <em>3</em>(3), 78-88.<br><strong>Ebola:</strong> World Health Organization (2020). <a href=\"https://www.who.int/en/news-room/fact-sheets/detail/ebola-virus-disease\">Ebola virus disease: Factsheet</a>.</p>\n\n\n\n<h2>Acknowledgements</h2>\n\n\n\n<p>We would like to acknowledge and thank a number of people in the development of this work: <strong>Carl Bergstrom</strong>, <strong>Bernadeta Dadonaite</strong>, <strong>Natalie Dean</strong>, <strong>Joel Hellewell,</strong> <strong>Jason Hendry</strong>, <strong>Adam Kucharski</strong>, <strong>Moritz Kraemer</strong> and <strong>Eric Topol</strong> for their very helpful and detailed comments and suggestions on earlier versions of this work. We thank <strong>Tom Chivers</strong> for his editorial review and feedback.</p>\n\n\n\n<p>And we would like to thank the many hundreds of readers who give us feedback on this work. Your feedback is what allows us to continuously clarify and improve it. We very much appreciate you taking the time to write. We cannot respond to every message we receive, but we do read all feedback and aim to take the many helpful ideas into account.</p>\n\n\n\n<p></p>\n",
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