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Coronavirus Disease (COVID-19) – Statistics and Research

We thank Bernadeta Dadonaite, Jason Hendry, and Moritz Kraemer for helpful comments and suggestions on earlier versions of this work.
Tom Chivers we would like to thank for editorial review and feedback.

And we would like to thank the many hundreds of you who give us feedback on this work every day.
Your feedback is what allows us to continuously clarify and improve it. We very much appreciate you taking the time to write.
Even if we can’t respond to every message we receive, we do read all feedback and take it all into account.

Note: To inform yourself and understand the risk to the public we recommend to rely on your government body responsible for health and the World Health Organization – their site is here.


The mission of Our World in Data is to make data and research on the world’s largest problems understandable and accessible.

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Only on the basis of clearly presented and well-documented data can governments, organizations and individuals hope to respond appropriately to the COVID-19 pandemic. The goal of our work here is to present the best available data and clarify what can – and can not be said – based on this data.

We list all our visualizations – more than 40 in total – on the pandemic on this page.


This article covers a developing situation and the Our World in Data team is updating it daily: The last update was made on April 8, 2020 (11:30, London time).

We rely on data from the European CDC

In this document and the many embedded and linked charts we report and visualize the data from the European Center for Disease Prevention and Control (ECDC).

The European CDC publishes daily statistics on the COVID-19 pandemic. Not just for Europe, but for the entire world.

The ECDC makes all their data available in a daily updated clean downloadable file. This gets updated daily reflecting data collected up to 6:00 and 10:00 CET. The data made public via the downloadable data file is published at 1pm CET, and is used to produce a page that gets updated daily under the name Situation Update Worldwide.

We discuss our data sources and why we rely on the data from the ECDC rather than other institutions at the end of this article here.

Reported new cases on a particular day do not necessarily represent new cases on that day

The number of confirmed cases or deaths reported by any institution – including the WHO, the ECDC, Johns Hopkins and others – on a given day does not represent the actual number of new cases or deaths on that date. This is because of the long reporting chain that exists between a new case or death, and its inclusion in national or international statistics.

The steps in this chain are different across countries, but for many countries the reporting chain includes most of the following steps:1

  1. Doctor or laboratory diagnoses a COVID-19 case based on testing or combination of symptoms and epidemiological probability (such as a close family member testing positive).
  2. Doctor or laboratory submits report to health department of the city or local district.
  3. Health department receives the report and records each individual case in the reporting system, including patient information.
  4. The ministry or another governmental organization brings this data together and publishes the latest figures.
  5. International data bodies such as the WHO or the ECDC can then collate statistics from hundreds of such national accounts.

This reporting chain can take several days. This is why the figures reported on any given date do not necessarily reflect the number of new cases or deaths on that specific date.

All our charts on Coronavirus Disease (COVID-19) – Statistics and Research

Deaths from COVID-19

Confirmed deaths to date is what we know

What we know is the total number of confirmed deaths due to COVID-19 to date. Limited testing and challenges in the attribution of the cause of death means that the number of confirmed deaths may not be an accurate count of the true total number of deaths from COVID-19.

The European Center for Disease Prevention and Control (ECDC) publishes daily updates of confirmed deaths due to COVID-19. We rely on this data as explained above.

In an ongoing outbreak the final outcomes – death or recovery – for all cases is not yet known. The time from symptom onset to death ranges from 2 to 8 weeks for COVID-19.2 This means that some people who are currently infected with COVID-19 will die at a later date. As we explain below, this needs to be kept in mind when comparing the current number of deaths with the current number of cases.

What does the data on deaths and cases tell us about the mortality risk of COVID-19?

To understand the risks and respond appropriately we would also want to know the mortality risk of COVID-19 – the likelihood that someone who catches the disease will die from it.

We will look into this question in more detail further below in this article and explain that this requires us to know – or estimate – the number of total cases and the final number of deaths for a given infected population. Because these are not known, we discuss what the current data can and can not tell us about the risk of death (scroll there).

The growth rate of COVID-19 deaths

How long did it take for the number of confirmed deaths to double?

In an outbreak of an infectious disease it is important to not only study the number of deaths, but also the growth rate at which the number of deaths is increasing.

This is because even if the current numbers of deaths are small when compared with other diseases, a fast growth rate can lead to very large numbers rapidly.

To report the rate of change we focus on the question: How long did it take for the number of confirmed deaths to double?

Let’s take an example: if three days ago there had been 500 confirmed deaths in total, and today we have reached 1,000, then the doubling time is three days.3

The doubling time of deaths has changed and it will change in the future; we should not naively extrapolate the current doubling time to conclude how many people will die.4

If deaths go up by a fixed number over a fixed period – say, by 500 every two days – then we call that “linear” growth. But if they keep on doubling within a fixed time period – say, every three days – then we call that “exponential” growth.

Humans tend to think in terms of linear growth, and research shows that we find exponential growth hard to understand. Below, we’ll try to explain it in a way that makes it more intuitive.

Understanding exponential growth

To get a sense of the difference between linear and exponential growth, take a look at the visualization here. The numbers shown do not represent any actual data points: they’re simply an illustration.

Both lines start at value of 10 at time 0. The linear trend (in blue) increases by 10 at every time increment (10, 20, 30, 40, 50, 60).

The exponential growth line (in red) doubles each increment (10, 20, 40, 80, 160, 320).

Early on in the growth chart the absolute difference remains small, but over time the exponential growth leads to very large numbers – to see this pull the blue time slider below the chart slowly to the right.

If, during an outbreak, the time it takes for deaths to double remains constant, then the disease is spreading exponentially.

Under exponential growth 500 deaths grow to more than 1 million deaths after 11 doubling times.5 And after 10 more doubling times it would be 1 billion deaths.

This isn’t to say that we should expect to see numbers like that in the COVID-19 outbreak, but it is a reminder that exponential growth leads to very large numbers very quickly, even when starting from a low base.

Psychologists find that humans tend to think in linear growth processes (1, 2, 3, 4) even when it doesn’t appropriately describe the reality in front of our eyes. This is called exponential growth bias.6

Growth in COVID-19 deaths, by country

The table below shows how long it has taken for the number of deaths to double in each country for which we have data. The table also shows how the total number of confirmed deaths, and the number of daily new confirmed deaths, and how those numbers have changed over the last 14 days.

You can sort the table by any of the columns by clicking on the column header.

We break the data down by country because some countries – like China and Korea – have very substantial countermeasures in place, and new daily confirmed deaths have declined (see the chart here).

Many other countries do not have comparable measures in place, and numbers are quickly rising. If we just look at the global average, we will not see these differences.

Data: The data shown here is published by the European Center for Disease Prevention and Control (ECDC). Here is our documentation of the data and an option to download all data.

COVID-19 deaths by country

In the charts here, you can find data on the number of new deaths and the total number so far, for every country.

The data shown here is published by the European Center for Disease Prevention and Control (ECDC). Here is the documentation of the data and an option to download all data.

The figures do not necessarily show the number of people who had died by a given date, but how many deaths had been reported by that date. For example, data shown for 4th April presents the latest figures as of 10am CET on that day.

Total and daily new confirmed deaths

This chart shows two series. In blue the daily new confirmed deaths and in red the total sum of confirmed deaths.

This chart – as all others – is interactive.

By default the worldwide data is shown, with the option Change Country in the bottom left corner of the chart the same data can be shown for any country in the world.

Per capita: we also have the same chart relative to the size of each country’s population here.

Total confirmed deaths

This chart shows the total number of confirmed deaths due to COVID-19.

By default the worldwide data is shown, with the option  Add country it is possible to show and compare all countries in the world.

By switching from the linear to a logarithmic y-axis it is possible to compare the growth rate between countries – on a logarithmic scale the steepness of the line corresponds to the growth rate.

Per capita: we also have the same chart relative to the size of each country’s population here.

Daily confirmed deaths

These two charts show the daily number of confirmed deaths due to COVID-19.

One show the day-by-day number of confirmed deaths; the other shows the rolling three-day average.

With the  Add country  option it is possible to add any countries to compare the daily confirmed deaths across countries.

Per capita: we also have the same charts relative to the size of each country’s population here.

COVID-19 deaths relative to the size of the population

In some cases it helpful to know not just how many people have died in a given country, but how many have died compared to how many people actually live there. For instance, if 1,000 people died in Iceland, out of a population of about 340,000, that would have a far bigger impact than the same number dying in the USA, with its population of 331 million.7

This is why the two maps below show the deaths per million people of each country’s population.

Deaths per million were calculated by Our World in Data by dividing the ECDC’s numbers of deaths by population figures published in the United Nations’ World Population Prospects.

By clicking on any country on the map you see the change over time in this country.

Are deaths increasing at different rates in different countries?

Are the number of deaths rising faster in China, Italy, Spain, South Korea, or the US?

The charts above are not very useful to answer these types of questions, because the outbreak of COVID-19 did not happen, and did not increase rapidly, on the same day in all countries.

The chart shown here is designed to allow these comparisons.

This chart allows the reader to compare the trajectory of confirmed deaths between countries. The starting point for each country is the day that particular country had reached 5 total confirmed deaths from COVID-19.

On the x-axis we see the days since the 5th confirmed death, and on the y-axis, the total number of confirmed deaths.

The grey lines show trajectories for a doubling time of 2 days, 3 days, 5 days and 10 days. Countries that follow a steeper rise have seen a doubling time faster than that.

Per capita: we also have the same chart relative to the size of each country’s population here.

Are countries bending the curve?

The previous chart looked at the total number of deaths.

This chart here is looking at the daily number of deaths.

To bring the pandemic to an end countries have to bend the curve – they have to achieve a plateau then decline in the number of daily cases.

This chart shows whether countries have achieved this.

Testing for COVID-19

Why data on testing is needed

Without data on COVID-19 we cannot possibly understand how the pandemic is progressing.

Without data we cannot respond appropriately to the threat; neither as individuals nor as a society. Nor can we learn where countermeasures against the pandemic are working. 

The number of confirmed cases is what informs us about the development of the pandemic.

But the confirmation of a case is based on a test. The World Health Organization defines a confirmed case as “a person with laboratory confirmation of COVID-19 infection”.8

Reliable data on testing is therefore necessary to assess the reliability of the data that informs us about the spread of the pandemic: the data on cases and deaths.

Some countries provide clear and helpful data on testing

Some countries present comprehensive, detailed and regularly updated data. Iceland (here) is one of these countries. Estonia (here) goes even further, showing breakdowns by age, gender and region.

For many countries however, available data on testing is either incomplete or else completely unavailable. This makes it impossible for their citizens and for researchers to assess the extent and significance of their testing efforts. 

Our current knowledge of COVID-19 testing – and more importantly of the pandemic itself – would be greatly improved if all countries were able to report all the testing data available to them in the way shown by the best examples. 

We need to understand what the published numbers on testing mean 

Those countries that do publish testing data often do not provide the required documentation to make it clear what the provided numbers precisely mean, and this is crucial for meaningful comparisons between countries and over time.

The key questions that any data description on testing data should answer are given in the following checklist. Clear answers to these questions are what is needed to properly interpret and compare published numbers.

For citizens to trust and understand the published data and for countries to learn from each other, it is crucial that every country provides the data on testing in a clearly documented way. We hope this checklist offers helpful guidance.

Our checklist for COVID-19 testing data

1) Is there any data?

Many countries are not yet providing official figures. Others do not do so on a regular basis. The first question to ask, then, is if there is any testing data for a given country.

Equally important is to make the available data findable. Currently, the available data is often not easy to find, because some countries are releasing figures at unpredictable intervals in ad-hoc locations (including social media or press conferences).

2) Do numbers refer to ‘performed tests’ or ‘individuals tested’?

The number of tests performed is different to the number of individuals tested. The reason for this is that it is common for COVID-19 testing that the same person is tested more than once.

Some countries report tests performed, while others report the number of individuals tested.

The source description should state clearly what is counted.

3) Are negative results included? Are pending results included?

It needs to be clear whether or not figures for the total number of tests performed, or the number of people tested, include negative test results, as well as the number of tests that are pending results.

Many sources report the number of individuals who are ‘suspected’ or have been ‘ruled out’. To be reliably included in test counts, it needs to be explicit whether such categories reflect the number of people who are awaiting test results or have tested negatively.

4) Do the figures include all tests conducted in the country, or only some? 

Figures reported by countries may only be partial if not all laboratories are reporting to the central authority.

The scope of testing data should be made explicit by the source. For instance, the US CDC make it clear that their figures do not include tests conducted in private labs.

5) Are all regions and laboratories within a country submitting data on the same basis?

Answers to the questions above may vary from region to region. In order to assess the reliability of aggregate testing data, it needs to be clear if heterogenous data is being summed together.

The US COVID Tracking Project, for instance makes it clear that their US totals combine data for tests performed and individuals tested, depending on which is reported by individual states.

6) What period do the published figures refer to?

Cumulative counts of the total number of tests should make clear the date from which the count begins. The key question that needs to be answered is whether the figures published at some date (attempt to) include all tests conducted up to that date.

Because the reporting of tests can take several days, for some countries figures for the last few days may not yet be complete. It needs to be made clear by the source if this may be the case. The US CDC, for instance, makes this clear.

7) Are there any issues that affect the comparability of the data over time?

If we want to look at how testing figures are changing over time, we need to know how any of the factors discussed above may have changed too.

The Netherlands, for instance, makes it clear that not all labs were included in national estimates from the start. As new labs get included, their past cumulative total gets added to the day they begin reporting, creating spikes in the time series.

8) What are the typical testing practices in the country?

Having a sense of how often and when individuals are tested, can help the users of these statistics understand how estimates of tests performed and individuals tested might relate to each other.

For instance, how many tests does a case investigation require? What are the eligibility criteria to be tested? Are health workers, or other specific groups, being routinely retested?

9) Might any of the information above be lost in translation?

People accessing data published in a language in which they are not fluent may misinterpret the data by mistranslating the provided text, which often includes technical terms.

Many countries report testing data in multiple languages – this helps disseminate the information to a broader audience, whilst helping prevent misinterpretations.

Our database on COVID-19 testing data

Our goal at Our World in Data is to provide testing data over time for many countries around the world.

We have started with this effort and will expand it in the coming days.

Our aim is to provide alongside the data a good understanding of the definitions used and any important limitations they might have. The checklist above is what guides our efforts.

At the end of this document you find descriptions of the data for each country. But in many cases sources do not yet provide the detailed descriptions of the data we would like. All the details we have been able to find so far are provided below.

We will be adding to the list of countries shown in the coming days.

The total number of tests performed or people tested so far

The two charts shown here show the total number of tests, or people tested, as indicated in the legend for each series.

The first chart shows the absolute number and the second shows it per thousand people of the country’s population.

For comparisons across the series it is important to understand the definitions of the different measures. These are provided in the country by country notes below.

Download the data: we make our full testing dataset, alongside detailed source descriptions, available on Github.

Tests per day

The two charts shown here show the daily number of tests, or people tested, in absolute terms and per thousand people respectively.

For comparisons across the series it is important to understand the definitions of the different measures. These are provided in the country by country notes below.

Download the data: we make our full testing dataset, alongside detailed source descriptions, available on Github.

Testing data: Source information country by country

Our detailed source information on all testing data can be found here.

Cases of COVID-19

The number of total cases is what we want to know, but their number is not known

To understand the scale of the COVID-19 outbreak, and respond appropriately, we would want to know how many people are infected by COVID-19. We would want to know the total number of cases.

However, the total number of COVID-19 cases is not known. When media outlets claim to report the ‘number of cases’ they are not being precise and omit to say that it is the number of confirmed cases they speak about.

The total number of cases is not known, not by us at Our World in Data, nor by any other research, governmental or reporting institution.

Confirmed cases is what we do know

What we do know is the number of confirmed cases.

A confirmed case is “a person with laboratory confirmation of COVID-19 infection” as World Health Organization (WHO) explains.9 But specifics can differ and the European CDC, on which we rely, reports confirmed cases according to the applied case definition in the countries.10

What is important however is that the number of confirmed cases is certainly not the same as the number of total cases. Confirmed cases are therefore only a subset of the total number of cases. It is a count of only those people who have COVID-19 and for whom a lab has confirmed this diagnosis. For this reason we emphasized the importance of testing in the section before.

The total number of confirmed cases is of course also not the same as the total number of all current cases. This is because for some of them the disease has ended and they have either recovered or died from it. We discuss how long the disease lasts further below (scroll there).

Why is the number of confirmed cases lower than the number of total cases?

The number of confirmed cases is lower than the number of total cases because not everyone is tested. Not all cases have a “laboratory confirmation”, testing is what makes the difference between the number of confirmed and total cases.

All countries have been struggling to test a large number of cases, which meant that not every person that should have been tested, has in fact been tested.

Since an understanding of testing for COVID-19 is crucial for an interpretation of the reported numbers of confirmed cases we have looked into the testing for COVID-19 in more detail.

You find our work on testing further below in this document (click here to scroll there).

The growth rate of COVID-19 cases

As for the number of deaths, it is not only important to study the number of cases, but also how they increase over time. Their growth rate.

To report the rate of change we focus on the question: How long did it take for the number of confirmed cases to double?

In this section, we’ll look at the numbers of confirmed cases in different countries. Just as with the number of deaths from the disease, we are interested not only in the absolute numbers, but in how quickly they grow, because the nature of exponential growth means that even if a country has a small number of cases now, they could increase very quickly.

Again as we did with deaths, we focus on the question: How long did it take for the number of confirmed cases to double? For instance, if three days ago there had been 5,000 cases reported to date in the United Kingdom, and today there have been a total of 10,000, we would say that the doubling time is three days.

To report the rate of change we focus on the question: How long did it take for the number of confirmed cases to double?

Growth in COVID-19 cases, by country

The table here shows how long it has taken for the number of cases to double in each country for which we have data. The table also shows how the total number of confirmed cases, and the number of daily new confirmed cases, and how those numbers have changed over the last 14 days.

You can sort the table by any of the columns by clicking on the column header.

We break the data down by country because some countries – like China and Korea – have very substantial countermeasures in place, and new daily confirmed cases have declined (see the chart here).

Many other countries do not have comparable measures in place, and numbers are quickly rising. If we just look at the global average, we will not see these differences.

Data: The data shown here is published by the European Center for Disease Prevention and Control (ECDC). Here is our documentation of the data and an option to download all data.

COVID-19 cases by country

In the charts here, you can find data on the number of new deaths and the total number so far, for every country.

The data shown here is published by the European Center for Disease Prevention and Control (ECDC). Here is the documentation of the data and an option to download all data.

The figures do not necessarily show the number of people who had died by a given date, but how many deaths had been reported by that date. For example, data shown for 4th April presents the latest figures as of 10am CET on that day.

Total and daily new confirmed COVID-19 cases

This chart shows two series. In orange the daily new confirmed cases and in red the total sum of confirmed cases.

This chart – as all others – is interactive.

By default the worldwide data is shown, with the option Change Country in the bottom left corner of the chart the same data can be shown for any country in the world.

Per capita: we also have the same chart relative to the size of each country’s population here.

Total confirmed cases

This chart shows the total number of confirmed COVID-19 cases.

By default the worldwide data is shown, with the option   Add country  it is possible to show and compare all countries in the world.

By switching from the linear to a logarithmic y-axis it is possible to compare the growth rate between countries – on a logarithmic scale the steepness of the line corresponds to the growth rate.

Per capita: we also have the same chart relative to the size of each country’s population here.

Daily confirmed cases

These two charts show the daily number of confirmed COVID-19 cases.

One show the day-by-day number of confirmed cases; the other shows the rolling three-day average.

With the  Add country  option it is possible to add any countries to compare the daily confirmed cases across countries.

Per capita: we also have the same charts relative to the size of each country’s population here.

COVID-19 cases relative to the size of the population

In some cases it helpful to know not just how many cases have been detected in a given country, but how many have been detected compared to how many people actually live there.

For instance, if 1,000 cases had been detected in Iceland, out of a population of about 340,000 that would have a far bigger impact than the same number being detected in the USA, with its population of 331 million.11

Cases per million were calculated by Our World in Data by dividing the ECDC’s numbers of deaths by population figures published in the United Nations’ World Population Prospects.

Are cases growing at different rates in different countries?

The COVID-19 outbreak started in different countries at different times, and now those countries are at different stages. For instance, on 25 March, Italy had reported 74,386 confirmed cases, while the UK had only reported 8,077.

But it would be useful to know whether cases in the UK now are growing faster, slower, at the same speed as cases did in Italy when it had a similar number.

This chart is designed to allow these comparisons, by showing how quickly the number of cases in each country has grown since the 100th confirmed case. That gives a standard starting place for each line on the graph.

China had a particular fast rise. Just 10 days after the 100th confirmed case the country already confirmed the 10,000th case.

Other countries saw a much slower increase. The speed at which the number of confirmed cases increased in Singapore and Japan was much slower than in other countries.

The straight grey lines show the trajectory for a doubling time of 2 days, 3 days, 5 days and 10 days. If a country’s line on the chart is higher than those lines, then its number of cases is doubling faster than that.

The pathway of China and South Korea shows that the speed at which cases rise is not necessarily constant over time. Both countries saw a rapid initial rise but then implemented severe countermeasures (see here), and the pathway became flatter, meaning that the spread of the disease has slowed down.

Per capita: we also have the same charts relative to the size of each country’s population here.

Are countries bending the curve of cases?

The previous chart looked at the total number of cases, this chart here shows the daily number of cases.

This trajectory chart shows whether countries achieve to bring down the curve of new cases.

Again, this is the number of confirmed cases rather than the total number of cases and it is important to keep in mind that levels and changes to the reported number of cases can be due to limited testing.

Confirmed cases vs deaths

In the sections above we focused on confirmed deaths and confirmed cases.

In these two charts it is possible to compare cases and deaths for any country in the world. First the totals, then the daily figures.

As always in these charts it is possible to switch to any other country in the world by choosing Change Country in the bottom left corner of the chart.

The COVID-19 pandemic

The name of the disease and the virus

Diseases and the viruses or bacteria that cause them often have different names. For instance, the “human immunodeficiency virus”, HIV, causes “acquired immunodeficiency syndrome”, AIDS.

The virus causing the current outbreak is called severe acute respiratory syndrome coronavirus 2, shortened to SARS-CoV-2. The disease is called coronavirus disease, shortened to COVID-19.

These names have been assigned by the World Health Organization and the International Committee on Taxonomy of Viruses.12

The WHO also refers to the virus as “the virus responsible for COVID-19” or “the COVID-19 virus” when communicating with the public. We follow the same conventions here.

How did the outbreak start?

The outbreak was first noticed in the city of Wuhan, China. Wuhan is the capital of the Hubei Province and has about 11 million inhabitants. On 29 December 2019, Chinese authorities identified a cluster of similar cases of pneumonia in the city.

These cases were soon determined to be caused by a novel coronavirus that was later named SARS-CoV-2.13

The first cases of COVID-19 outside of China were identified on January 13 in Thailand and on January 16 in Japan.

On January 23rd the city of Wuhan and other cities in the region were placed on lockdown by the Chinese Government.

Since then COVID-19 has spread to many more countries – cases have been reported in all world regions. By March it developed into a global pandemic and was declared as such by the WHO.

What is a coronavirus?

Although people often refer to the virus causing COVID-19 as “the coronavirus”, there are many different coronaviruses.

The term refers to a group of viruses that are common in humans: coronaviruses are the cause for around 30% of cases of the common cold.14 Corona is Latin for “crown” – this group of viruses is given its name due to the fact that its surface looks like a crown under an electron microscope.

Two outbreaks of new diseases in recent history were also caused by coronaviruses – SARS in 2003 that resulted in around 1,000 deaths15 and MERS in 2012 that resulted in 862 deaths.16

What do we know about 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.

Here we explain why that is. We’ll discuss the “case fatality rate”, the “crude mortality rate”, and the “infection fatality rate”, and why they’re all different.

The key point is that the “case fatality rate”, the most commonly discussed measure of the risk of dying, is not the answer to the question, for two reasons. One, it relies on the number of confirmed cases, and many cases are not confirmed; and two, it relies on the total number of deaths, and with COVID-19, some people who are sick and will die soon have not yet died. These two facts mean that it is extremely difficult to make accurate estimates of the true risk of death.

The 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.17 This measure is sometimes called case fatality risk or case fatality ratio, or CFR.

But this is not the same as the risk of death for an infected person – even though, unfortunately, journalists often suggest that it is. It is relevant and important, but far from the whole story.

The CFR is very easy to calculate. You take the number of people who have died, 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%.

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.

The 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 that we asked earlier: 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 called the crude death rate – measures the probability that any individual in the population will die from the disease; not just those who are infected, or are confirmed as being infected. 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%, even if only 100 people had been diagnosed with the disease.

This difference is important: unfortunately, people sometimes confuse case fatality rates with crude death rates. A common example is the Spanish flu pandemic in 1918. One estimate, by Johnson and Mueller (2002), is that that pandemic killed 50 million people.18 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 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 look at the global death count of this pandemic and others here.]

What we want to know isn’t the case fatality rate: it’s the infection fatality rate

Before we look at what the CFR does tell us about the mortality risk, it is helpful to see what it doesn’t.

Remember the question we asked at the beginning: if someone is infected with COVID-19, how likely is it that they will die? 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%.19,20,21,22,23

To work out the IFR, we need two numbers: the total number of cases and the total number of deaths. 

However, as we explain here, the total number of cases of COVID-19 is not known. That’s partly because not everyone with COVID-19 is tested.24,25 

We may be able to estimate the total number of cases and use it to calculate the IFR – and researchers do this. But the total number of cases is not known, so the IFR cannot be accurately calculated. And, despite what some media reports imply, the CFR is not the same as – or, probably, even similar to – the IFR. Next, we’ll discuss why.

Interpreting the case fatality rate

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 journalists talk about the CFR as if it’s a single, steady number, an unchanging fact about the disease. This is a particular bad example from the New York Times in the early days of the COVID-19 outbreak.

But it’s not a biological constant; instead, it reflects the severity of the disease 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 that 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. 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 2020.26

It shows the CFR values 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 centre 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 says that that is 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)27
Covid cfr in china over time

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 (unknown) probability?

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), 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.

And whenever there are cases of the disease that are not counted, then 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 are 500 real cases, then the real risk (the IFR) is just 2%.

Or 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. This of course assumes that there is not also significant undercounting in the number of deaths; it’s plausible that some deaths are missed or go unreported, but we’d expect the magnitude of undercounting to be less than for cases.

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.

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. With COVID-19, many of those who are currently sick and will die have not yet died. Or, they may die from the disease but be listed as having died from something else.

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 eventually be counted as a death too. This means the CFR right now is an underestimate of what it will be when the disease has run its course.

With the COVID-19 outbreak, it can take between two to eight weeks for people to go from first symptoms to death, according to data from early cases (we discuss this here).28 

This is not a problem once an outbreak has finished. Afterwards, the total number of deaths will be known, and we can use it to calculate the CFR. But during an outbreak, we need to be careful with how to interpret the CFR because the outcome (recovery or death) of a large number of cases is still unknown.

This is a common source for misinterpretation of a rising CFR in the earlier stages of an outbreak.29

This is what happened during the SARS-CoV outbreak in 2003: the CFR was initially reported to be 3-5% during the early stages of the outbreak, but had risen to around 10% by the end.30,31

This is not just a problem for statisticians: it had real negative consequences for our understanding of the outbreak. The low numbers that were published initially resulted in an underestimate of the severity of the outbreak. And the rise of the CFR over time gave the wrong impression that SARS was becoming more deadly over time. These errors made it harder to come up with the right response.

The current case fatality rate of COVID-19

We should stress again that there is no single figure of CFR for any particular disease. The CFR varies by location, and is typically changing over time.

As this paper shows32, CFRs vary widely between countries, from 0.2% in Germany to 7.7% in Italy. But it says that this is not necessarily an accurate comparison of the true likelihood that someone with COVID-19 will die of it.

We do not know how many cases are asymptomatic versus symptomatic, or whether the same criteria for testing are being applied between countries. Without better and more standardised criteria for testing and for the recording of deaths, the real mortality rate is unknown. As the paper says, to understand the differences in CFR and how they should guide decision-making, we need better data.

But if we’re careful to acknowledge its limitations, CFR can help us to better understand the severity of the disease and what we should do about it.

This chart shows how these early CFR values compare. You can see the total number of confirmed cases of COVID-19 (on the x-axis, going across) versus the total number of deaths (on the y-axis, going up).

The grey lines show a range of CFR values – from 0.25% to 10%.

Where each country lies indicates its CFR – for instance, if a country lies along the 2% line, its current confirmed cases and death figures indicate it has a CFR of 2%.

The second chart shows how the CFR has changed over time in countries that have had over 100 confirmed cases.

We have excluded countries which still have a relatively small number of confirmed cases, because CFR is a particularly poor metric to understand mortality risk with a small sample size.

We see this if we look at the trajectory of cases and deaths in Iran: on February 24th it had 2 confirmed cases and 2 deaths, an implausible CFR of 100%. With time its CFR begins to fall, as the number of confirmed cases increases. By the time it has seen hundreds of cases, the CFR drops to around the level seen in other countries.

Case fatality rate of COVID-19 by age

Current data across countries suggests that the elderly are most at risk

It’s helpful to estimate the risk of death across a population – the average IFR, the chance of death if a random person in the country were to catch the disease, which we discussed above. It helps us know the severity of an outbreak.

But during an outbreak, it’s also crucial to know which groups within a population are most at risk. If we know which sections of society are most likely to die, or suffer other serious consequences, then that allows us to direct our resources towards the most vulnerable, who need them the most.

In the chart below, we see a breakdown of the CFR by age group across various countries who have made demographic data on confirmed cases and deaths available. It shows very large differences of the CFR by age. 

This data is based on the number of confirmed cases and deaths in each age group as reported by national agencies. The figures come from the Chinese Center for Disease Control and Prevention (CDC) as of 17th February; Spanish Ministry of Health as of 24th March; Korea Centers for Disease Control and Prevention (KCDC) as of 24th March; and the Italian National Institute of Health, as presented in the paper by Onder et al. (2020) as of 17th March.33,34

Again it’s important to stress that the CFR simply represents the number of deaths divided by the number of confirmed cases. It does not tell us the true risk of death, which (as we say above) is much harder to estimate. The CFR changes over time, and differences between countries do not necessarily reflect real differences in the risk of dying from COVID-19. Instead, they may reflect differences in the extent of testing, or the stage a country is in its trajectory through the outbreak.

For many infectious diseases young children are most at risk. For instance, in the case of malaria, the majority of deaths (57% globally) are in children under five. The same was true for the largest pandemic in recorded history: During the ‘Spanish flu’ in 1918, children and young adults were at the greatest risk from the pandemic (we write more about this in the article here).

For COVID-19 cases the opposite seems to be true. The elderly are at the greatest risk of dying, if infected with this virus.

It may not simply be that the older you get, the more at risk you are, though. As we show in the next section, the CFR for people with underlying health conditions – such as cardiovascular diseases, respiratory diseases or diabetes – is higher than for those without. Elderly people are more likely to have those conditions, which is likely to be part of the reason why the elderly are most at risk from COVID-19. 

Covid cfr by age

Case fatality rate of COVID-19 by preexisting health conditions

Early data from China suggests that those with underlying health conditions are at a higher risk

The chart here shows the case fatality rate for populations within China based on their health status or underlying health condition.

This is based on the same data from the Center for Disease Control and Prevention as we discussed in the section on age.35 This analysis was based on recorded deaths and cases in China in the period up to February 11th 2020.

The researchers found that the CFR for those with an underlying health condition is much higher than for those without. For instance, more than 10% of people with a cardiovascular disease, and who were diagnosed with COVID-19, died. Diabetes, chronic respiratory diseases, hypertension, and cancer were all risk factors as well, as we see in the chart.

By comparison, the CFR was 0.9% – more than ten times lower – for those without a preexisting health condition.

Above we saw that the elderly are most at risk of dying from COVID-19. As we said there, that might be partly explained by the fact that they are also most likely to have underlying health conditions such as cardiovascular disease, respiratory disease and diabetes; these health conditions make it more difficult to recover from the COVID-19 infection.

Coronavirus cfr by health condition in china

Case fatality rate of COVID-19 compared to other diseases

How does the case fatality rate (CFR) of COVID-19 compare to other virus outbreaks and 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.36

The US seasonal flu has a case fatality rate of approximately 0.1% – much lower than the current CFR for COVID-19.

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. 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. New England Journal of Medicine, 382(8), 692-694.
Seasonal flu: US Centers for Disease Control and Prevention (CDC). Influenza Burden, 2018-19.
Ebola: Shultz, J. M., Espinel, Z., Espinola, M., & Rechkemmer, A. (2016). Distinguishing epidemiological features of the 2013–2016 West Africa Ebola virus disease outbreak. Disaster Health, 3(3), 78-88.
Ebola: World Health Organization (2020). Ebola virus disease: Factsheet.

DiseaseEstimated case fatality rate (CFR)
SARS-CoV10%
Venkatesh and Memish (‎2004)‎
Munster et al. (2020)
MERS-CoV34%
Munster et al. (2020)
Seasonal flu (US)0.1-0.2%
US CDC
Ebola50%
40% in the 2013-16 outbreak

WHO (2020)
Shultz et al. (2016)

Healthcare capacity

The capacity of the healthcare system is of great importance in any country’s response to the pandemic. If there are more critically ill people than there are intensive care facilities, people will die who otherwise might not have.

The two maps here show the number of medical doctors and hospital beds relative to the size of each country’s population.

Countries with more elderly people may be hardest hit

As we saw above, the current evidence suggests that older people are at a higher risk from COVID-19 than younger ones. That means that countries with more older people can expect to suffer worse consequences than those with more younger ones, all else being equal.

The map shows the share of the population that is 70 years and older.

In our entry on the age structure we study this in much more detail.

About this page

Limitations of current research and limitations of our presentation of current research

The purpose of this article on COVID-19 is to aggregate existing research, bring together the relevant data and allow readers to make sense of the published data and early research on the coronavirus outbreak.

Most of our work focuses on established problems, for which we can refer to well-established research and data. COVID-19 is different. All data and research on the virus is preliminary; researchers are rapidly learning more about a new and evolving problem. It is certain that the research we present here will be revised in the future. But based on our mission we feel it is our role to present clearly what the current research and data tells us about this emerging problem and especially to provide an understanding of what can and cannot be said based on this available knowledge.

As always in our work, one important strategy of dealing with this problem is to always link to the underlying original research and data so that everyone can understand how this data was produced and how we arrive at the statements we make. But scrutiny of all reported research and data is very much required. We welcome your feedback. In the current situation we read and consider all feedback, but can not promise to reply to all.

Our data sources

Our World in Data relies on data from the European CDC

In this document and the associated charts we report and visualize the data from the European Center for Disease Prevention and Control (ECDC). Established in 2005 and based in Stockholm it is an EU agency with the aim to strengthen Europe’s defense against infectious diseases.

The European CDC publishes daily statistics on the COVID-19 pandemic. Not just for Europe, but for the entire world.

The European CDC collects and aggregates data from countries around the world. The most up-to-date data for any particular country is therefore typically available earlier via the national health agencies than via the ECDC.

This lag between nationally available data and the ECDC data is not very long as the ECDC publishes new data daily. But it can be several hours.

We rely on the ECDC as they collect and harmonize data from around the world which allows us to compare what is happening in different countries. The European CDC data provides a global perspective on the evolving pandemic.

The ECDC makes all their data available in a daily updated clean downloadable file. This gets updated daily reflecting data collected up to 6:00 and 10:00 CET. The data made public via the downloadable data file is published at 1pm CET, and is used to produce a page that gets updated daily under the name Situation Update Worldwide.

Why we stopped relying on data from the World Health Organization

Until March 18 we relied on the World Health Organization (WHO) as our source. We aimed to rely on the WHO as they are the international agency with the mandate to provide official estimates on the pandemic. The WHO reports this data for each single day and they can be found here at the WHO’s site.

Since March 18 it became unfortunately impossible to rely on the WHO data to understand how the pandemic is developing over time. With Situation Report 58 the WHO shifted the reporting cutoff time from 0900 CET to 0000 CET. This means that comparability is compromised because there is an overlap between these two WHO data publications (Situation Reports 57 and 58).

Additionally we found many errors in the data published by the WHO when we went through all the daily Situation Reports. We immediately notified the WHO and are in close contact with the WHO’s team to correct the errors that we pointed out to them. We document all errors we found. The main problem we see with the WHO data is that these errors are not communicated by the WHO itself (some Errata were published by the WHO – in the same place as the Situation Reports –, but most errors were either retrospectively corrected without public notice or remain uncorrected).

Here is our detailed documentation of where the WHO’s data is sourced from and how we corrected its data – we also provide several options to download all corrected data there. As of March 18 we no longer maintain this database for the reason that the WHO data can not be used for reliable time-series information.

Data and dashboards from other sources

The World Health Organization (WHO), researchers from Johns Hopkins University, and other institutions all maintain datasets on the number of cases, deaths, and recoveries from the disease.

These are presented in a number of useful dashboards and websites listed below.

Johns Hopkins data on COVID-19

A dashboard is published and hosted by researchers at the Center for Systems Science and Engineering, Johns Hopkins University. It shows the number and location of confirmed COVID-19 cases, deaths, and recoveries in all affected countries.

The researchers have the intention to “continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak”.

Scientific Paper: The background paper for the Johns Hopkins’ dashboard was published by Dong, Du, and Gardner (2020) in The Lancet Infectious Disease.37 This paper also includes a comparison of this data with the data reported by the WHO and the Chinese CDC.

Data: All collected data in this effort from the Johns Hopkins University is made freely available by the researchers through this GitHub repository. You can download all the data shown in the dashboard. Information on the sources of their data can also be found directly there.

Link: Here is the Johns Hopkins dashboard. And here is a mobile friendly version of the same dashboard.

WHO data on COVID-19

The World Health Organization (WHO) publishes a dashboard similar to that of Johns Hopkins above.

The WHO dashboard on global cases and deaths is embedded here. In this dashboard it is possible to see up-to-date country specific data.

In addition to this dashboard, the WHO publishes daily Situation Reports which can be found here. It is the daily Situation reports that we rely on in our own published datasets on case and death numbers. Unlike the daily Situation Reports, the WHO dashboard is updated three times per day: any inconsistencies between the WHO dashboard and the data we present will be explained by this fact.

As we explained above, the Our World in Data team found several minor errors in the WHO data – we documented these errors, corrected them, reported them to the WHO, and are in close contact with colleagues at the WHO. Here is the documentation of our adjustments to the WHO data and an option to download all data.

Link to the WHO dashboard.

nCoV-2019 Data Working Group data

The nCoV-2019 Data Working Group, which includes colleagues from the University of Oxford, publishes epidemiological data from the outbreak via this global dashboard. From this dashboard it is possible to obtain the underlying data which includes demographic and epidemiological descriptions of a long list of individual cases.

Their data on the list of cases includes individual travel history and key dates for each patient – date of onset of symptoms, date of hospitalisation and date of laboratory confirmation of whether the person was infected with the COVID-19 virus or not.

This data is intended to be helpful in the estimation of key statistics for the disease: Incubation period, basic reproduction number (R0), age-stratified risk, risk of importation.

In previous disease outbreaks such global individual data was not openly available.

Data from the Chinese Center for Disease Control and Prevention

The Chinese Center for Disease Control and Prevention publishes data via their dedicated site ‘Tracking the epidemic‘.

The mission of Our World in Data is to make the best research on the world’s largest problems available and understandable. While most of our work focuses on large problems that humanity has faced for a long time – such as child mortalitynatural disasterspoverty and almost 100 other problems (see here) – this article focuses on a new, emerging global problem: the ongoing outbreak of the coronavirus disease [COVID-19].

  • Pneumonia – Severe cases of COVID-19 can progress to pneumonia.38 Our entry on pneumonia provides an overview of the data and research on this disease that kills 2.6 million annually.
  • Age Structure – Since the mortality risk for COVID-19 varies by age, the age structure of the population matters for the risk that the disease poses to the population. This entry looks in detail at the age structure of countries around the world.
  • Vaccination – Vaccines are key in making progress against infectious diseases and save millions of lives every year. A vaccine against COVID-19 would be the scientific breakthrough that could end this pandemic.
  • Healthcare financing – A strong healthcare system is key to make progress against the infectious disease. In this entry we are studying how healthcare is financed.
  • Causes of death – 56 million people die every year. What do they die from? How did the causes of death change over time?
  • Smoking – In this entry we study the prevalence of smoking and its global health impact. Smoking is a risk factor for COVID-19 as it increases the chance of getting infected and can increase the severity of the disease.39