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.

Read more about our mission →


The COVID-19 outbreak is an unprecedented global public health challenge. In order for governments, organisations and individuals to respond to it effectively, it will be vital that they have easy access to good, clear data and a good understanding of what can and can not be said based on the available data.

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].

We present information on the number of deaths and cases; on why we should focus on how quickly those numbers double, not on the numbers themselves; on the prevalence of testing, and why that is important for understanding the disease; and what we can and can’t know about how lethal COVID-19 really is. We’ll also explain where we get our data from and why.

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 March 31, 2020 (13:00, 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.

It is important to note that the number of confirmed cases or deaths reported by the ECDC 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.

This article – here – discusses the length of this reporting chain in detail for Germany, and how this affects the timing of reported statistics. The steps in this chain will be similar across most other countries. For a confirmed case to be reported in national or international statistics, it has to go through most or all of the following steps:

  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 manually in the electronic system, including important patient information.
  4. Local or city health departments submit reports to state departments.
  5. State health departments send updates to their ministry. Press office of the ministry then develops communication strategy and publishes the latest figures.
  6. International data bodies such as the WHO or the ECDC can then collate statistics from all national accounts.

This official 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.

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-19.1 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.2

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.3

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.4 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.5

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.

Confirmed 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.

These charts are interactive. The data is shown as the worldwide figures by default, but can be explored by country by clicking either on + Add Country or Change Country within the chart.

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 25th March presents the latest figures as of 10am CET on 25th March.

Confirmed 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.6

This is why the chart and 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 cases 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.

We also show the trajectory of confirmed deaths adjusted for population size – here presented as the number of deaths per million people. This is shown from the day that a given country reached 0.1 deaths per million people.

Relative to the size of the population: This chart shows the trajectories for deaths per million.

Testing for COVID-19

One of the most important things that countries are doing to help understand and stop the spread of COVID-19 is testing. Here we explain why testing is important, what it involves, and how many tests countries are doing based on available data from official sources.

It’s important to remember that these numbers change very quickly, so they’re provisional and uncertain; but data about testing is extremely important, and at the moment there is no central database compiling them, so we are trying to put together the best available numbers.

Why is testing important?

Testing allows infected people to know that they are infected. This can help them receive the care they need; and it can help them take measures to reduce the probability of infecting others. People who don’t know they are infected might not stay at home and thereby risk infecting others.

Testing is also crucial for an appropriate response to the pandemic. It allows us to understand the spread of the disease and to take evidence-based measures to slow down the spread of the disease.7

Unfortunately, the capacity for COVID-19 testing is still low in many countries around the world. For this reason we still do not have a good understanding of the spread of the pandemic.

To understand the impact of COVID-19, we need to know things like how easily it spreads, and how dangerous it is for people who have it. So that means we need to know how many people are infected. That’s difficult, because the disease can look a lot like other illnesses, like flu, and in some people the symptoms are very mild. 

To understand how infectious the disease is, and how dangerous, we need to test people to see if they have it. For instance, if you know that 100 people have died from it, it makes a difference whether that’s out of 1,000 people who have had the disease, or 100,000. The best way to fight against a disease that spreads very easily but doesn’t kill many of those infected is very different to the best way to fight against a disease that spreads more slowly but is more deadly.

Also, testing allows infected people to get the care they need and to take precautions. If they don’t know they’re infected, they might not stay home, and put other people at risk.

Perhaps most importantly, testing lets healthcare workers identify people with the disease, and help isolate them and the people they’ve been in contact with.8

Unfortunately, many countries still don’t have enough capacity for COVID-19 testing. That shortage is one reason why we still don’t fully understand how the disease spreads.

Unfortunately, the capacity for COVID-19 testing is still low in many countries around the world. For this reason we still do not have a good understanding of the spread of the pandemic.

How are COVID-19 tests done?

The most common tests for COVID-19 involve taking a swab from a patient’s nose and throat and checking them for the genetic footprint of the virus. They are called “PCR tests”. The first PCR tests for COVID-19 were developed very rapidly – within two weeks of the disease being identified. They are now part of the World Health Organisation (WHO)’s recommended protocol for dealing with the disease.9

Here you can find an explainer video on how the tests for coronavirus disease work.

The tests are not perfect: sometimes, people who have the disease will be wrongly told that they do not

Some people require more than one test because of false-negative outcomes

The number of COVID-19 tests carried out will be similar to the number of people tested, but they won’t be quite the same, because some people may need to be tested multiple times. The reason for this is that there are “false-negative” test outcomes.10,11,12,13 

A “false-negative” outcome is when someone is tested and found to be clear of the disease, but when tested again, are found to have it. The WHO’s guidelines for laboratory testing of COVID-19 say that negative results “do not rule out the possibility of COVID-19 virus infection.”14

This means that even in countries that have done lots of tests, the true number of COVID-19 cases is still uncertain, although of course more testing still means more certainty.

There haven’t been many studies into how common false negatives are, so it’s hard to know how big an impact they have on our understanding – but research is going on.15

Why might COVID-19 tests fail?

There are several reasons why someone infected with COVID-19 may produce a false-negative result when tested:16,17

  • They may be in the early stage of the disease with a viral load that is too low to be detected.
  • They may have no major respiratory symptoms, so there could be little detectable virus in the patient’s throat and nose.
  • There may have been a problem with sample collection, meaning there was very little sample to test.
  • There may have been poor handling and shipping of samples and test materials.
  • There may have been technical issues inherent in the test, e.g. virus mutation.

The WHO suggests that these issues should be taken into account and that for some people, tests should be carried out several times.18

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.19 But specifics can differ and the European CDC, on which we rely, reports confirmed cases according to the applied case definition in the countries.20

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).

Growth of cases: How long did it take for the number of confirmed cases to double?

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.

Confirmed COVID-19 cases by country

In our visualizations here you can explore the number of total confirmed cases and daily new confirmed cases for all countries with reported cases. This is shown in absolute numbers, and adjusted for population size by showing total and new confirmed cases per million people.

These charts are interactive: the data is shown as the worldwide figures by default but can be explored by country – by clicking on + Add Country or Change country within the chart.

Data: 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 date here reflects to the date of reporting, not necessarily the confirmed case figures on that given day. For example, data shown for 25th March presents the latest figures as of 10am CET on 25th March.

Confirmed 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 360,000, that would have a far bigger impact than the same number being detected in the USA, with its population of 327 million.

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. The line is called a “trajectory”.

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 trajectories 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 trajectory 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 trajectory became flatter, meaning that the spread of the disease has slowed down.

We also show the trajectory of confirmed cases adjusted for population size – here presented as the number of confirmed cases per million people. This is shown from the day that a given country reached 1 confirmed case per million people.

Relative to the size of the population: This chart shows the trajectories for cases per million.

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.21

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-2.22

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.23 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 deaths24 and MERS in 2012 that resulted in 862 deaths.25

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.26 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.27 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.]

Before we consider what the CFR tells us about the mortality risk it is helpful to see what the CFR does not tell us.

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

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%.28,29,30,31,32

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.33,34 

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

As this chart shows, the case fatality rate of COVID-19 is not constant. This chart was published in the Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19), in February 2020.35

It shows the CFR values for COVID- in several locations in China during the early stages of the outbreak, from the beginning of January 2020 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. The WHO says that “the standard of care has evolved over the course of the outbreak”. The CFR fell to 0.7% for patients with the onset of symptoms after February 1st.

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 more 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)36
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.

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 will mean the CFR is lower than the true risk.

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).37 

That means that some people who are now counted as confirmed cases and who will die are not yet included in the number of deaths. This means the CFR right now is an underestimate of what it will be when the disease has run its course.

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.38

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.39,40

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 published in The Lancet highlights clearly: better data is needed to give a clear understanding of the differences in CFR and how they should guide decision-making.41

The paper compares the CFR of different countries – showing a very broad range from 0.2% in Germany to 7.7% in Italy. But it states clearly that this does not necessarily give an accurate comparison of the probability of dying from COVID-19 if someone is infected. We do not know how many cases are asymptomatic versus symptomatic; and whether the same criteria for testing are being applied. Without better and more homogenous criteria for testing and recording of deaths, the real mortality rate is unknown.

But with a good understanding of the measure and its limitations, CFR can be helpful for understanding what we currently know about the severity of the disease and for responding accordingly.

In the chart shown here we can see how these early CFR values compare. It shows the total number of confirmed cases of COVID-19 (on the x-axis) versus the total number of deaths (on the y-axis). Since the CFR is the ratio between the total deaths and total confirmed cases, we can use this comparison to see where each country would lie in terms of its CFR.

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

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

With these caveats in mind, the other visualization here shows the CFR for countries which have more than 100 confirmed cases. This means we have excluded countries which still have a relatively small number of confirmed cases: this is 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, which would have a CFR of 100%. With time its CFR begins to fall as the number of confirmed cases increases, but it’s not until it reaches hundreds of cases that the CFR falls below 20%.

Case fatality rate of COVID-19 by age

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

The total population-level estimate of the case fatality rate (CFR) above is useful for understanding the average severity of an outbreak, but does not tell us who within a population is most at risk. But this understanding is crucial in an outbreak. Understanding the relative risk to different sections of a population allows us to focus on the most vulnerable, and improve the allocation of health resources to those who need them most.

The Chinese Center for Disease Control and Prevention has published an analysis of recorded cases and deaths in China for the period until February 11th 2020 which provides a breakdown of all known cases, deaths and the CFR by specific demographics (age, sex, preexisting condition etc.).42

In the visualization 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.43,44

Again it’s important to stress that the CFR simply represents the ratio between the number of confirmed cases and the number of deaths. The CFR continues to change over time, and comparisons between countries do not necessarily reflect differences in the likelihood of dying from COVID-19; they can instead 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. We see this for malaria: the majority of deaths (57% globally) are in children under five years of age. The same was true for the largest pandemic in recorded history: During the ‘Spanish flu’ in 1918 it was primarily children and young adults who died 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.

As we show in the following section, the CFR for people with underlying health conditions is higher than for those without. One possible reason why the elderly might be most at risk is that they are also those who are most likely to have underlying health conditions such as cardiovascular diseases, respiratory diseases or diabetes. 

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 visualization 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’s initial breakdown of cases, deaths and CFR among specific demographics in the population.45 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.

More than 10% of those diagnosed with COVID-19 who already had a cardiovascular disease, died as a result of the virus. Diabetes, chronic respiratory diseases, hypertension, and cancer were all risk factors as well, as we see in the chart.

The CFR was 0.9% for those without a preexisting health condition.

Above we saw that the elderly are most at risk of dying from COVID-19. This 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.46

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%
US CDC
Ebola50%
40% in the 2013-16 outbreak

WHO (2020)
Shultz et al. (2016)

How do case fatality rates from COVID-19 compare to those of the seasonal flu?

This question is answered in the visualization here. We compare the CFR during the outbreak of COVID-19 in China with the CFR of the US seasonal flu in 2018-19.

The case fatality rate of the seasonal flu in the US is around 0.1% to 0.2%, while the case fatality rate for COVID-19, measured in the cited study, was 2.3%.

The US data is sourced 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.

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 (shown in square brackets). 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. The CDC reports 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%.47

The CFRs for COVID-19 are again based on the numbers reported by the Chinese Center for Disease Control and Prevention.48 As before, the Chinese data refers to recorded deaths and confirmed cases in China as of February 11th 2020.

As we emphasise, the global CFR for COVID-19 continues to change over time, and can vary significantly by location.

While the CFR for COVID-19 is much higher than the CFR of the seasonal flu the two diseases are similar in the profile of the fatality rate by age: elderly populations have higher case fatality rates.

However, the CFR of COVID-19 is much higher for all age groups, including young people. On top of each bar we have indicated how much higher the CFR for COVID-19 is for each age group.

Covid 19 cfr by age vs. us seasonal flu 3

Healthcare capacity

To respond to the pandemic, the capacity of the healthcare system is of great importance.

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

Age structure

The discussion of the age-specific mortality above showed that the current evidence suggests that older population are at a higher risk from COVID-19.

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.49 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 by selecting the country in the top right.

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‘.

  • Pneumonia – Severe cases of COVID-19 can progress to pneumonia.50 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.51