How many people died from coronavirus
How many people died from coronavirus
Coronavirus (COVID-19) Deaths
We are grateful to everyone whose editorial review and expert feedback on this work helps us to continuously improve our work on the pandemic. Thank you. Here you find the acknowledgements.
The data on the coronavirus pandemic is updated daily.
Our work belongs to everyone
This page provides data on the number of confirmed deaths from COVID-19.
We know – based on reports and estimates of excess deaths – that these figures underestimate the total impact of the pandemic on mortality globally. We provide data on excess deaths across the world here:
Explore the global data on confirmed COVID-19 deaths
Select countries to show in all charts
This page has a large number of charts on the pandemic. In the box below you can select any country you are interested in – or several, if you want to compare countries.
All charts on this page will then show data for the countries that you selected.
Confirmed deaths
What is the daily number of confirmed deaths?
Related charts:
Which world regions have the most daily confirmed deaths?
This chart shows the number of confirmed COVID-19 deaths per day.
Three points on confirmed death figures to keep in mind
All three points are true for all currently available international data sources on COVID-19 deaths:
→ We provide more detail on these three points in the section ‘Deaths from COVID-19: background‘.
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Daily confirmed deaths per million people
Why adjust for the size of the population?
Differences in the population size between countries are often large, and the COVID-19 death count in more populous countries tends to be higher. Because of this it can be insightful to know how the number of confirmed deaths in a country compares to the number of people who live there, especially when comparing across countries.
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 United States, with its population of 331 million. 1 This difference in impact is clear when comparing deaths per million people of each country’s population – in this example it would be roughly 3 deaths/million people in the US compared to a staggering 2,941 deaths/million people in Iceland.
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What is the cumulative number of confirmed deaths?
Related charts:
Which world regions have the most cumulative confirmed deaths?
The previous charts looked at the number of confirmed deaths per day – this chart shows the cumulative number of confirmed deaths since the beginning of the COVID-19 pandemic.
Another tip on how you can interact with this chart
By pulling the ends of the blue time slider you can focus the chart on a particular period. If you bring them together to one point in time then the line chart becomes a bar chart – this of course only makes sense if you compare countries (that is what the
Add country button is for).
Cumulative confirmed deaths per million people
This chart shows the cumulative number of confirmed deaths per million people.
Weekly and biweekly deaths: where are confirmed deaths increasing or falling?
Why is it useful to look at weekly or biweekly changes in deaths?
For all global data sources on the pandemic, daily data does not necessarily refer to deaths on that day – but to the deaths reported on that day.
Since reporting can vary significantly from day to day – irrespectively of any actual variation of deaths – it is helpful to look at changes from week to week. This provides a slightly clearer picture of where the pandemic is accelerating, slowing, or in fact reducing.
The maps shown here provide figures on weekly and biweekly deaths: one set shows the number of deaths per million people in the previous seven or fourteen days (the weekly or biweekly cumulative total); the other set shows the percentage change (growth rate) over these periods.
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Global comparison: where are confirmed deaths increasing most rapidly?
Simply looking at the cumulative total or daily number of confirmed deaths does not allow us to understand or compare the speed at which these figures are rising.
The table here shows how long it has taken for the number of confirmed deaths to double in each country for which we have data. The table also shows both the cumulative total and daily new number of confirmed deaths, and how those numbers have changed over the last 14 days.
A tip on how to interact with this table
You can sort the table by any of the columns by clicking on the column header.
Confirmed deaths and cases: our data source
Our World in Data relies on data from Johns Hopkins University
The Johns Hopkins University dashboard and dataset is maintained by a team at its Center for Systems Science and Engineering (CSSE). It has been publishing updates on confirmed cases and deaths for all countries since January 22, 2020. A feature on the JHU dashboard and dataset was published in The Lancet in early May 2020. 2 This has allowed millions of people across the world to track the course and evolution of the pandemic.
JHU updates its data multiple times each day. This data is sourced from governments, national and subnational agencies across the world — a full list of data sources for each country is published on Johns Hopkins’s GitHub site. It also makes its data publicly available there.
Deaths from COVID-19: background
What is counted as a death from COVID-19?
The attribution of deaths to specific causes can be challenging under any circumstances. Health problems are often connected, and multiplicative, meaning an underlying condition can often lead to complications which ultimately result in death.
This is also true in the case of COVID-19: the disease can lead to other health problems such as pneumonia and acute respiratory distress syndrome (ARDS).
So, how are deaths from COVID-19 recorded? What is and isn’t included in these totals?
As is standard in death reporting, countries are asked to follow the ‘cause of death’ classifications from the WHO’s International Classification of Diseases guidelines. 3 However, countries also typically provide their own guidance to practitioners on how and when COVID-19 deaths should be recorded.
Let’s take a look at two concrete examples of national guidance: the United States and the UK. Both provide very similar guidelines for medical practitioners on the completion of death certificates. Here is the US CDC’s Vital Statistics Reporting Guidance; here is the UK Government guidance. 4
Both guidelines state that if the practitioner suspects that COVID-19 played a role in an individual’s death it should be specified on the death certificate. In some cases, COVID-19 may be the underlying cause of death, having led to complications such as pneumonia or ARDS. Even when it’s the underlying and not the direct cause, COVID-19 should be listed. 5
Although confirmed cases are reliant on a positive laboratory confirmation of the COVID-19 test, a laboratory diagnosis may not be required for it to be listed as the cause of death. In the UK guidelines, for example, it makes clear that practitioners should complete death certificates to the best of their knowledge, stating that “if before death the patient had symptoms typical of COVID-19 infection, but the test result has not been received, it would be satisfactory to give ‘COVID-19’ as the cause of death, and then share the test result when it becomes available. In the circumstances of there being no swab, it is satisfactory to apply clinical judgement.”
This means a positive COVID-19 test result is not required for a death to be registered as COVID-19. In some circumstances, depending on national guidelines, medical practitioners can record COVID-19 deaths if they think the signs and symptoms point towards this as the underlying cause.
The US CDC guidelines also make this clear with an example: the death of an 86-year-old female with an unconfirmed case of COVID–19. It was reported that the woman had typical COVID-19 symptoms five days prior to suffering an ischemic stroke at home. Despite not being tested for COVID-19, the coroner determined that the likely underlying cause of death was COVID–19 given her symptoms and exposure to an infected individual.
Why are there delays in death reports?
Just as with confirmed cases, the number of deaths reported on a given day does not necessarily reflect the actual number of COVID-19 deaths on that day, or in the previous 24 hours. This is due to lags and delays in reporting.
Delays can occur for several reasons:
The delay in reporting can be on the order of days and sometimes as long as a week or more. This means the number of deaths reported on a given day is not reflective of the actual number of deaths that occurred on that day.
Actual death figures are likely to be higher than confirmed deaths
What we know is the 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 actual number of deaths from COVID-19.
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. 6 This means that some people who are currently infected with COVID-19 will die at a later date. 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 is infected with the disease will die from it.
We look into this question in more detail on our page about the mortality risk of COVID-19.
Acknowledgements
We would like to acknowledge and thank a number of people in the development of this work: Carl Bergstrom, Bernadeta Dadonaite, Natalie Dean, Joel Hellewell, Jason Hendry, Adam Kucharski, Moritz Kraemer and Eric Topol for their very helpful and detailed comments and suggestions on earlier versions of this work. We thank Tom Chivers for his editorial review and feedback.
And we would like to thank the many hundreds of readers who give us feedback on this work. Your feedback is what allows us to continuously clarify and improve it. We very much appreciate you taking the time to write. We cannot respond to every message we receive, but we do read all feedback and aim to take the many helpful ideas into account.
Endnotes
Here is our visualization for the population of Iceland and the US. Any other country can be added to this chart.
National Center for Health Statistics. Guidance for certifying deaths due to COVID–19. Hyattsville, MD. 2020.
The WHO, in its ICD documentation, defines the underlying cause of death as “a) the disease or injury which initiated the train of morbid events leading directly to death, or b) the circumstances of the accident or violence which produced the fatal injury.”
World Health Organization (2020). Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Available online at: https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf
Reuse our work freely
All visualizations, data, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.
The data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.
Citation
Our articles and data visualizations rely on work from many different people and organizations. When citing this entry, please also cite the underlying data sources. This entry can be cited as:
Coronavirus (COVID-19) Mortality Rate
Last updated: May 14, 22:00 GMT
Introduction
When calculating the mortality rate, we need:
Considering that a large number of cases are asymptomatic (or present with very mild symptoms) and that testing has not been performed on the entire population, only a fraction of the SARS-CoV-2 infected population is detected, confirmed through a laboratory test, and officially reported as a COVID-19 case. The number of actual cases is therefore estimated to be at several multiples above the number of reported cases. The number of deaths also tends to be underestimated, as some patients are not hospitalized and not tested.
If we base our calculation (deaths / cases) on the number of reported cases (rather than on the actual ones), we will greatly overestimate the fatality rate.
Fatality Rate based on New York City Actual Cases and Deaths
Worldometer has analyzed the data provided by New York City, the New York State antibody study, and the excess deaths analysis by the CDC. Combining these 3 sources together we can derive the most accurate estimate to date on the mortality rate for COVID-19, as well as the mortality rate by age group and underlying condition. These findings can be valid for New York City and not necessarily for other places (suburban or rural areas, other countries, etc.), but they represent the best estimates to date given the co-occurrence of these 3 studies.
Actual Cases (1.7 million: 10 times the number of confirmed cases)
New York State conducted an antibody testing study [source], showing that 12.3% of the population in the state had COVID-19 antibodies as of May 1, 2020. The survey developed a baseline infection rate by testing 15,103 people at grocery stores and community centers across the state over the preceding two weeks. The study provides a breakdown by county, race (White 7%, Asian 11.1%, multi/none/other 14.4%, Black 17.4%, Latino/Hispanic 25.4%), and age, among other variables. 19.9% of the population of New York City had COVID-19 antibodies. With a population of 8,398,748 people in NYC [source], this percentage would indicate that 1,671,351 people had been infected with SARS-CoV-2 and had recovered as of May 1 in New York City. The number of confirmed cases reported as of May 1 by New York City was 166,883 [source], more than 10 times less.
Actual Deaths (23,000: almost twice the number of confirmed deaths)
As of May 1, New York City reported 13,156 confirmed deaths and 5,126 probable deaths (deaths with COVID-19 on the death certificate but no laboratory test performed), for a total of 18,282 deaths [source]. The CDC on May 11 released its «Preliminary Estimate of Excess Mortality During the COVID-19 Outbreak — New York City, March 11–May 2, 2020» [source] in which it calculated an estimate of actual COVID-19 deaths in NYC by analyzing the «excess deaths» (defined as «the number of deaths above expected seasonal baseline levels, regardless of the reported cause of death») and found that, in addition to the confirmed and probable deaths reported by the city, there were an estimated 5,293 more deaths to be attributed. After adjusting for the previous day (May 1), we get 5,148 additional deaths, for a total of actual deaths of 13,156 confirmed + 5,126 probable + 5,148 additional excess deaths calculated by CDC = 23,430 actual COVID-19 deaths as of May 1, 2020 in New York City.
Infection Fatality Rate (23k / 1.7M = 1.4% IFR)
Actual Cases with an outcome as of May 1 = estimated actual recovered (1,671,351) + estimated actual deaths (23,430) = 1,694,781.
Infection Fatality Rate (IFR) = Deaths / Cases = 23,430 / 1,694,781 = 1.4% (1.4% of people infected with SARS-CoV-2 have a fatal outcome, while 98.6% recover).
Mortality Rate (23k / 8.4M = 0.28% CMR to date) and Probability of Dying
As of May 1, 23,430 people are estimated to have died out of a total population of 8,398,748 in New York City. This corresponds to a 0.28% crude mortality rate to date, or 279 deaths per 100,000 population, or 1 death every 358 people. Note that the Crude Mortality Rate will continue to increase as more infections and deaths occur (see notes under the paragraph «Herd Immunity» below for details).
Mortality Rate by Age
When analyzing the breakdown of deaths by age and condition [source], we can observe how, out of 15,230 confirmed deaths in New York City up to May 12, only 690 (4.5% of all deaths) occurred in patients under the age of 65 who did not have an underlying medical condition (or for which it is unknown whether they had or did not have an underlying condition).
Underlying illnesses include Diabetes, Lung Disease, Cancer, Immunodeficiency, Heart Disease, Hypertension, Asthma, Kidney Disease, GI/Liver Disease, and Obesity [source]
Under 65-year-old (0.09% CMR to date)
85.9% of the population (7,214,525 people out of 8,398,748) in New York City is under 65 years old according to the US Census Bureau, which indicates the percent of persons 65 years old and over in New York City as being 14.1% [source].
We don’t know what percentage of the population in this age group has an underlying condition, so at this time we are not able to accurately estimate the fatality rate for the under 65 years old and healthy.
But we can calculate it for the entire population under 65 years old (both healthy and unhealthy): with 6,188 deaths (26% of the total deaths in all age groups) occurring in this age group, of which 5,498 deaths (89%) in patients with a known underlying condition, the crude mortality rate to date will correspond to 6,188 / 7,214,525 = 0.09% CMR, or 86 deaths per 100,000 population (compared to 0.28% and 279 deaths per 100,000 for the general population).
So far there has been 1 death every 1,166 people under 65 years old (compared to 1 death every 358 people in the general population). And 89% of the times, the person who died had one or more underlying medical conditions.
NOTE: We are gathering and analyzing additional data in order to provide more estimates by age group.
Herd Immunity and final Crude Mortality Rate
Crude mortality rate is not really applicable during an ongoing epidemic.
And to reach herd immunity for COVID-19 and effectively end the epidemic, approximately two thirds (67%) of the population would need to be infected. As of May 1, New York City is at 20%, based on the antibody study findings.
Therefore, the crude mortality rate has the potential to more than triple from our current estimate, reaching close to 1,000 deaths per 100,000 population (1% CMR), and close to 300 per 100,000 (0.3% CMR) in the population under 65 years old, with 89% of these deaths (267 out of 300) occurring in people with a known underlying medical condition (including obesity).
Historical Account of the Initial Estimates
3.4% Mortality Rate estimate by the World Health Organization (WHO) as of March 3
In his opening remarks at the March 3 media briefing on Covid-19, WHO Director-General Dr Tedros Adhanom Ghebreyesus stated:
“Globally, about 3.4% of reported COVID-19 cases have died. By comparison, seasonal flu generally kills far fewer than 1% of those infected.” [13]
Initial estimate was 2%
Initially, the World Health Organization (WHO) had mentioned 2% as a mortality rate estimate in a press conference on Wednesday, January 29 [1] [2] and again on February 10. However, on January 29 WHO specified that this was a very early and provisional estimate that might have changed. Surveillance was increasing, within China but also globally, but at the time it was said that:
Mortality Rate as of Feb. 20 in China (findings from the Report of the WHO-China Joint Mission)
The Report of the WHO-China Joint Mission published on Feb. 28 by WHO [12] is based on 55,924 laboratory confirmed cases. The report notes that «The Joint Mission acknowledges the known challenges and biases of reporting crude CFR early in an epidemic» (see also our discussion on: How to calculate the mortality rate during an outbreak). Here are its findings on Case Fatality Ratio, or CFR:
«As of 20 February, 2,114 of the 55,924 laboratory confirmed cases have died (crude fatality ratio [ CFR: 3.8% ) (note: at least some of whom were identified using a case definition that included pulmonary disease).
The overall CFR varies by location and intensity of transmission (i.e. 5.8% in Wuhan vs. 0.7% in other areas in China ).
In China, the overall CFR was higher in the early stages of the outbreak ( 17.3% for cases with symptom onset from 1-10 January) and has reduced over time to 0.7% for patients with symptom onset after 1 February. » [12]
Mortality Rate, as discussed by the National Health Commission (NHC) of China on Feb. 4
Asked at a press conference on February 4 what the current mortality rate (or case fatality rate, CFR) is, an official with China NHC said that [7] :
Preliminary study providing a tentative 3% estimate for case fatality rate
A preliminary study published on The Lancet on January 24 [3] provided an early estimation of 3% for the overall case fatality rate. Below we show an extract (highlights added for the relevant data and observations):
Of the 41 patients in this cohort, 22 (55%) developed severe dyspnoea and 13 (32%) required admission to an intensive care unit, and six died.
Hence, the case-fatality proportion in this cohort is approximately 14.6%, and the overall case fatality proportion appears to be closer to 3%.
However, both of these estimates should be treated with great caution because not all patients have concluded their illness (ie, recovered or died) and the true number of infections and full disease spectrum are unknown.
Importantly, in emerging viral infection outbreaks the case-fatality ratio is often overestimated in the early stages because case detection is highly biased towards the more severe cases.
As further data on the spectrum of mild or asymptomatic infection becomes available, one case of which was documented by Chan and colleagues, the case-fatality ratio is likely to decrease.
Nevertheless, the 1918 influenza pandemic is estimated to have had a case-fatality ratio of less than 5% but had an enormous impact due to widespread transmission, so there is no room for complacency.
Fatality rate can also change as a virus can mutate, according to epidemiologists.
Death rate among patients admitted to hospital
A study on 138 hospitalized patients with 2019-nCoV infection, published on February 7 on JAMA, found that 26% of patients required admission to the intensive care unit (ICU) and 4.3% died, but a number of patients were still hospitalized at the time. [9]
A previous study had found that, out of 41 admitted hospital patients, 13 (32%) patients were admitted to an ICU and six (15%) died. [5]
Days from first symptom to death
The Wang et al. February 7 study published on JAMA found that the median time from first symptom to dyspnea was 5.0 days, to hospital admission was 7.0 days, and to ARDS was 8.0 days. [9]
Previously. the China National Health Commission reported the details of the first 17 deaths up to 24 pm 22 Jan 2020. A study of these cases found that the median days from first symptom to death were 14 (range 6-41) days, and tended to be shorter among people of 70 year old or above (11.5 [range 6-19] days) than those with ages below 70 year old (20 [range 10-41] days. [6]
Median Hospital Stay
The JANA study found that, among those discharged alive, the median hospital stay was 10 days. [9]
Comparison with other viruses
For comparison, the case fatality rate with seasonal flu in the United States is less than 0.1% (1 death per every 1,000 cases).
Mortality rate for SARS was 10%, and for MERS 34%.
How to calculate the mortality rate during an outbreak
At present, it is tempting to estimate the case fatality rate by dividing the number of known deaths by the number of confirmed cases. The resulting number, however, does not represent the true case fatality rate and might be off by orders of magnitude [. ]
A precise estimate of the case fatality rate is therefore impossible at present.
The case fatality rate (CFR) represents the proportion of cases who eventually die from a disease.
Once an epidemic has ended, it is calculated with the formula: deaths / cases.
But while an epidemic is still ongoing, as it is the case with the current novel coronavirus outbreak, this formula is, at the very least, «naïve» and can be «misleading if, at the time of analysis, the outcome is unknown for a non negligible proportion of patients.» [8]
In other words, current deaths belong to a total case figure of the past, not to the current case figure in which the outcome (recovery or death) of a proportion (the most recent cases) hasn’t yet been determined.
The correct formula, therefore, would appear to be:
CFR = deaths at day.x / cases at day.x-
This would constitute a fair attempt to use values for cases and deaths belonging to the same group of patients.
One issue can be that of determining whether there is enough data to estimate T with any precision, but it is certainly not T = 0 (what is implicitly used when applying the formula current deaths / current cases to determine CFR during an ongoing outbreak).
Let’s take, for example, the data at the end of February 8, 2020: 813 deaths (cumulative total) and 37,552 cases (cumulative total) worldwide.
If we use the formula (deaths / cases) we get:
813 / 37,552 = 2.2% CFR (flawed formula).
With a conservative estimate of T = 7 days as the average period from case confirmation to death, we would correct the above formula by using February 1 cumulative cases, which were 14,381, in the denominator:
Feb. 8 deaths / Feb. 1 cases = 813 / 14,381 = 5.7% CFR (correct formula, and estimating T=7).
T could be estimated by simply looking at the value of (current total deaths + current total recovered) and pair it with a case total in the past that has the same value. For the above formula, the matching dates would be January 26/27, providing an estimate for T of 12 to 13 days. This method of estimating T uses the same logic of the following method, and therefore will yield the same result.
An alternative method, which has the advantage of not having to estimate a variable, and that is mentioned in the American Journal of Epidemiology study cited previously as a simple method that nevertheless could work reasonably well if the hazards of death and recovery at any time t measured from admission to the hospital, conditional on an event occurring at time t, are proportional, would be to use the formula:
CFR = deaths / (deaths + recovered)
which, with the latest data available, would be equal to:
6,454,202 / (6,454,202 + 544,907,396) = 1% CFR (worldwide)
If we now exclude cases in mainland China, using current data on deaths and recovered cases, we get:
6,448,976 / (6,448,976 + 544,682,953) = 1.2% CFR (outside of mainland China)
The sample size above is limited, and the data could be inaccurate (for example, the number of recoveries in countries outside of China could be lagging in our collection of data from numerous sources, whereas the number of cases and deaths is more readily available and therefore generally more up to par).
There was a discrepancy in mortality rates (with a much higher mortality rate in China) which however is not being confirmed as the sample of cases outside of China is growing in size. On the contrary, it is now higher outside of China than within.
That initial discrepancy was generally explained with a higher case detection rate outside of China especially with respect to Wuhan, where priority had to be initially placed on severe and critical cases, given the ongoing emergency.
Unreported cases would have the effect of decreasing the denominator and inflating the CFR above its real value. For example, assuming 10,000 total unreported cases in Wuhan and adding them back to the formula, we would get a CFR of 1.2% (quite different from the CFR of 1% based strictly on confirmed cases).
Neil Ferguson, a public health expert at Imperial College in the UK, said his “best guess” was that there were 100,000 affected by the virus even though there were only 2,000 confirmed cases at the time. [11]
Without going that far, the possibility of a non negligible number of unreported cases in the initial stages of the crisis should be taken into account when trying to calculate the case fatally rate.
As the days go by and the city organized its efforts and built the infrastructure, the ability to detect and confirm cases improved. As of February 3, for example, the novel coronavirus nucleic acid testing capability of Wuhan had increased to 4,196 samples per day from an initial 200 samples. [10]
A significant discrepancy in case mortality rate can also be observed when comparing mortality rates as calculated and reported by China NHC: a CFR of 3.1% in the Hubei province (where Wuhan, with the vast majority of deaths is situated), and a CFR of 0.16% in other provinces (19 times less).
Finally, we shall remember that while the 2003 SARS epidemic was still ongoing, the World Health Organization (WHO) reported a fatality rate of 4% (or as low as 3%), whereas the final case fatality rate ended up being 9.6%.
Coronavirus Pandemic (COVID-19)
The data on the coronavirus pandemic is updated daily.
Explore all metrics – including cases, deaths, testing, and vaccinations – in one place.
Get an overview of the pandemic for any country on a single page.
Download our complete dataset of COVID-19 metrics on GitHub. It’s open access and free for anyone to use.
Explore our global dataset on COVID-19 vaccinations.
See state-by-state data on vaccinations in the United States.
Explore the data on confirmed COVID-19 cases for all countries.
Explore the data on confirmed COVID-19 deaths for all countries.
Explore our data on COVID-19 testing to see how confirmed cases compare to actual infections.
See data on how many people are being hospitalized for COVID-19.
See how government policy responses – on travel, testing, vaccinations, face coverings, and more – vary across the world.
Learn what we know about the mortality risk of COVID-19 and explore the data used to calculate it.
Compare the number of deaths from all causes during COVID-19 to the years before to gauge the total impact of the pandemic on deaths.
Explore the global situation
Coronavirus Country Profiles
We built 207 country profiles which allow you to explore the statistics on the coronavirus pandemic for every country in the world.
In a fast-evolving pandemic it is not a simple matter to identify the countries that are most successful in making progress against it. For a comprehensive assessment, we track the impact of the pandemic across our publication and we built country profiles for 207 countries to study in depth the statistics on the coronavirus pandemic for every country in the world.
Each profile includes interactive visualizations, explanations of the presented metrics, and the details on the sources of the data.
Every country profile is updated daily.
Our 12 most visited country profiles
Every profile includes five sections:
Acknowledgements
We would like to acknowledge and thank a number of people in the development of this work: Carl Bergstrom, Bernadeta Dadonaite, Natalie Dean, Joel Hellewell, Jason Hendry, Adam Kucharski, Moritz Kraemer and Eric Topol for their very helpful and detailed comments and suggestions on earlier versions of this work. We thank Tom Chivers for his editorial review and feedback.
And we would like to thank the many hundreds of readers who give us feedback on this work. Your feedback is what allows us to continuously clarify and improve it. We very much appreciate you taking the time to write. We cannot respond to every message we receive, but we do read all feedback and aim to take the many helpful ideas into account.
Reuse our work freely
All visualizations, data, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.
The data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.
Citation
Our articles and data visualizations rely on work from many different people and organizations. When citing this entry, please also cite the underlying data sources. This entry can be cited as:
Онлайн карта распространения коронавируса Covid-19
Общая мировая статистика заражения на сегодня
Данные Всемирной организации здравоохранения по ситуации с распространением коронавируса в мире на 14.08.2022.
| Подтверждено +770 236 (за сутки) | Смертей +2 727 (за сутки) |
| 589 245 727 | 6 433 419 |
График распространения коронавируса на 14.08.2022
Ниже предоставлен актуальный график заражений по всему миру по дням. Отображает число заразившихся, умерших и выздоровевших людей по всему миру с 1 февраля 2020 года и на сегодняшний день. Наведите на нужную дату, чтобы посмотреть статистику.
Обновление данных статистики по дням осуществляется 1 раз в сутки, чтобы зафиксировать количество случаев на конец дня. Сегодня заболевание SARS-CoV-2 обнаружено в 212 странах мира.
График заражения коронавирусом в России по дням
Статистика заражения коронавирусом по странам
В данной онлайн-таблице размещена статистика коронавируса на сегодня по странам мира. В ней приведены количество заболевших коронавирусом, число умерших и выздоровевших в каждой стране мира. Информация из официальных источников.
| Страна | Подтверждено | Заболевших за сутки | Смертей | Погибло за день |
|---|---|---|---|---|
| США | 92 919 750 | 81 073 | 1 037 017 | 27 |
| Индия | 44 253 464 | 14 092 | 527 037 | 41 |
| Бразилия | 34 148 131 | 0 | 681 253 | 0 |
| Франция | 34 406 092 | 0 | 154 104 | 0 |
| Россия | 18 881 958 | 28 982 | 383 125 | 58 |
| Испания | 13 294 139 | 0 | 111 667 | 0 |
| Аргентина | 9 602 534 | 0 | 129 440 | 0 |
| Великобритания | 23 634 821 | 0 | 186 798 | 0 |
| Колумбия | 6 286 392 | 0 | 141 287 | 0 |
| Италия | 21 480 076 | 24 785 | 173 982 | 129 |
| Мексика | 6 925 668 | 49 960 | 328 724 | 287 |
| Перу | 4 013 831 | 0 | 214 890 | 0 |
| Южная Африка | 4 004 555 | 0 | 101 982 | 0 |
| Германия | 31 535 343 | 0 | 145 698 | 0 |
| Иран | 7 468 894 | 3 315 | 142 861 | 55 |
| Польша | 6 118 482 | 4 639 | 116 773 | 22 |
| Чили | 4 369 706 | 11 888 | 59 961 | 31 |
| Ирак | 609 039 668 | 0 | 25 326 | 0 |
| Бельгия | 4 453 483 | 0 | 32 364 | 0 |
| Украина | 5 304 634 | 485 | 116 508 | 2 |
| Индонезия | 6 278 332 | 5 104 | 157 208 | 19 |
| Чехия | 4 011 962 | 571 | 40 641 | 7 |
| Бангладеш | 2 008 644 | 144 | 29 312 | 0 |
| Нидерланды | 8 471 927 | 0 | 23 175 | 0 |
| Турция | 16 295 817 | 0 | 99 678 | 0 |
| Филиппины | 3 827 758 | 4 674 | 60 992 | 48 |
| Саудовская Аравия | 811 853 | 105 | 9 269 | 3 |
| Пакистан | 1 562 307 | 0 | 30 523 | 0 |
| Румыния | 3 154 721 | 4 684 | 66 336 | 25 |
| Израиль | 4 613 292 | 841 | 11 483 | 0 |
| Канада | 4 141 368 | 0 | 43 440 | 0 |
| Марокко | 1 263 373 | 117 | 1 037 017 | 1 |
| Швейцария | 3 994 397 | 0 | 13 939 | 0 |
| Непал | 992 727 | 162 | 11 986 | 2 |
| Португалия | 5 376 433 | 0 | 24 722 | 0 |
| Эквадор | 975 234 | 0 | 35 811 | 0 |
| Австрия | 4 861 952 | 0 | 20 485 | 0 |
| Швеция | 2 551 996 | 0 | 19 528 | 0 |
| ОАЭ | 1 003 929 | 800 | 2 339 | 0 |
| Боливия | 1 079 371 | 2 556 | 22 126 | 6 |
| Панама | 960 300 | 0 | 8 434 | 0 |
| Катар | 418 622 | 583 | 681 | 0 |
| Кувейт | 655 854 | 0 | 2 562 | 0 |
| Доминиканская Республика | 633 689 | 0 | 4 384 | 0 |
| Иордания | 1 720 778 | 0 | 14 090 | 0 |
| Венгрия | 2 005 399 | 0 | 46 966 | 0 |
| Коста-Рика | 1 057 695 | 0 | 8 780 | 0 |
| Оман | 397 231 | 0 | 4 628 | 0 |
| Казахстан | 1 465 506 | 1 465 | 19 030 | 0 |
| Гватемала | 1 072 241 | 1 915 | 19 223 | 18 |
| Армения | 428 648 | 0 | 8 637 | 0 |
| Япония | 15 478 389 | 183 526 | 35 045 | 256 |
| Египет | 515 645 | 0 | 24 781 | 0 |
| Беларусь | 994 037 | 0 | 7 118 | 0 |
| Гондурас | 447 365 | 0 | 10 954 | 0 |
| Эфиопия | 492 738 | 25 | 7 569 | 0 |
| Ливан | 1 195 784 | 1 443 | 10 572 | 6 |
| Венесуэла | 540 102 | 897 | 5 775 | 5 |
| Болгария | 1 226 064 | 818 | 37 486 | 1 |
| Китай | 7 198 225 | 29 393 | 24 207 | 43 |
| Молдавия | 551 862 | 0 | 11 662 | 0 |
| Бахрейн | 666 373 | 581 | 1 512 | 0 |
| Словакия | 2 580 748 | 0 | 20 236 | 0 |
| Тунис | 1 139 241 | 0 | 29 153 | 0 |
| Хорватия | 1 200 113 | 1 215 | 16 484 | 12 |
| Ливия | 505 956 | 0 | 6 434 | 0 |
| Сербия | 2 203 039 | 5 255 | 16 438 | 14 |
| Узбекистан | 243 509 | 22 | 1 637 | 0 |
| Парагвай | 712 907 | 0 | 19 357 | 0 |
| Азербайджан | 804 585 | 420 | 9 773 | 5 |
| Босния и Герцеговина | 390 650 | 0 | 15 942 | 0 |
| Грузия | 1 710 749 | 0 | 16 877 | 0 |
| Ирландия | 1 650 791 | 0 | 7 743 | 0 |
| Кения | 337 938 | 17 | 5 673 | 0 |
| Нигерия | 262 520 | 118 | 3 147 | 0 |
| Мьянма | 614 360 | 63 | 19 435 | 1 |
| Кыргызстан | 204 671 | 0 | 2 991 | 0 |
| Алжир | 269 008 | 142 | 6 878 | 0 |
| Греция | 4 577 675 | 0 | 31 722 | 0 |
| Сингапур | 1 791 046 | 4 403 | 1 556 | 4 |
| Дания | 3 298 716 | 0 | 6 841 | 0 |
| Гана | 168 350 | 0 | 1 458 | 0 |
| Словения | 168 350 | 0 | 1 458 | 0 |
| Малайзия | 4 732 502 | 4 334 | 36 080 | 10 |
| Афганистан | 188 820 | 116 | 7 758 | 3 |
| Северная Македония | 334 562 | 0 | 9 414 | 0 |
| Сальвадор | 190 818 | 0 | 4 217 | 0 |
| Литва | 1 200 279 | 1 532 | 9 230 | 0 |
| Южная Корея | 21 355 958 | 119 603 | 25 623 | 57 |
| Австралия | 9 794 953 | 16 113 | 12 862 | 42 |
| Норвегия | 1 458 057 | 69 | 3 834 | 0 |
| Албания | 321 345 | 564 | 3 570 | 1 |
| Черногория | 269 213 | 634 | 2 758 | 0 |
| Люксембург | 284 931 | 0 | 1 119 | 0 |
| Камерун | 120 967 | 0 | 1 933 | 0 |
| Финляндия | 1 238 998 | 0 | 5 350 | 0 |
| Мадагаскар | 66 557 | 0 | 1 409 | 0 |
| Замбия | 331 925 | 0 | 4 016 | 0 |
| Сенегал | 87 752 | 0 | 1 968 | 0 |
| Шри Ланка | 667 916 | 181 | 16 619 | 5 |
| Уганда | 169 396 | 0 | 3 628 | 0 |
| Судан | 63 128 | 0 | 4 960 | 0 |
| Мозамбик | 229 859 | 23 | 2 219 | 1 |
| Намибия | 169 253 | 0 | 4 073 | 0 |
| Ангола | 102 636 | 0 | 1 917 | 0 |
| Гвинея | 37 470 | 0 | 447 | 0 |
| Мальдивы | 184 591 | 0 | 307 | 0 |
| Таджикистан | 17 786 | 0 | 125 | 0 |
| Ямайка | 147 908 | 183 | 3 221 | 2 |
| Кабо-Верде | 62 253 | 4 | 410 | 0 |
| Гаити | 32 703 | 126 | 838 | 0 |
| Габон | 48 592 | 0 | 306 | 0 |
| Латвия | 881 020 | 0 | 5 909 | 0 |
| Зимбабве | 256 522 | 0 | 5 587 | 0 |
| Мавритания | 62 705 | 4 | 992 | 0 |
| Ботсвана | 325 824 | 0 | 2 774 | 0 |
| Мальта | 113 501 | 30 | 797 | 0 |
| Куба | 1 109 360 | 101 | 8 529 | 0 |
| Багамские острова | 36 905 | 24 | 823 | 1 |
| Эстония | 593 571 | 0 | 2 634 | 0 |
| Кипр | 571 994 | 0 | 1 149 | 0 |
| Сирия | 571 994 | 0 | 1 149 | 0 |
| Эсватини | 73 326 | 4 | 1 419 | 0 |
| Малави | 87 710 | 5 | 2 673 | 1 |
| Тринидад и Тобаго | 174 896 | 344 | 4 075 | 4 |
| Никарагуа | 14 872 | 0 | 244 | 0 |
| Джибути | 15 690 | 0 | 189 | 0 |
| Джибути | 15 690 | 0 | 189 | 0 |
| ДР Конго | 92 456 | 0 | 1 391 | 0 |
| Руанда | 132 354 | 11 | 1 466 | 0 |
| Суринам | 80 988 | 0 | 1 380 | 0 |
| Исландия | 203 518 | 0 | 179 | 0 |
| Экваториальная Гвинея | 16 892 | 0 | 183 | 0 |
| Гайана | 70 439 | 41 | 1 275 | 0 |
| Белиз | 67 630 | 0 | 680 | 0 |
| Сомали | 27 020 | 0 | 1 361 | 0 |
| Таиланд | 4 620 425 | 1 773 | 31 828 | 30 |
| Мали | 31 244 | 2 | 739 | 0 |
| Уругвай | 973 420 | 0 | 7 423 | 0 |
| Гамбия | 12 238 | 0 | 368 | 0 |
| Южный Судан | 17 823 | 0 | 138 | 0 |
| Бенин | 27 316 | 0 | 163 | 0 |
| Того | 38 273 | 0 | 281 | 0 |
| Буркина-Фасо | 21 128 | 0 | 387 | 0 |
| Гвинея-Бисау | 8 452 | 0 | 174 | 0 |
| Сьерра-Леоне | 7 740 | 0 | 126 | 0 |
| Йемен | 11 903 | 0 | 2 152 | 0 |
| Новая Зеландия | 1 693 914 | 2 759 | 1 751 | 0 |
| Чад | 7 444 | 0 | 193 | 0 |
| Либерия | 7 578 | 0 | 294 | 0 |
| Нигер | 9 132 | 0 | 311 | 0 |
| Вьетнам | 11 364 355 | 1 815 | 43 097 | 1 |
| Сан-Марино | 20 130 | 0 | 118 | 0 |
| Сан-Томе и Принсипи | 6 120 | 0 | 76 | 0 |
| Лихтенштейн | 18 907 | 0 | 86 | 0 |
| Бурунди | 48 002 | 0 | 38 | 0 |
| Папуа новая Гвинея | 44 820 | 0 | 663 | 0 |
| Коморские острова | 8 351 | 0 | 161 | 0 |
| Монако | 14 277 | 0 | 61 | 27 |
| Танзания | 38 205 | 0 | 841 | 0 |
| Эритрея | 10 134 | 1 | 103 | 0 |
| Маврикий | 248 720 | 0 | 1 019 | 0 |
| Монголия | 966 423 | 0 | 2 123 | 0 |
| Бутан | 60 663 | 0 | 21 | 0 |
| Камбоджа | 137 207 | 28 | 3 056 | 0 |
| Барбадос | 97 411 | 307 | 516 | 1 |
| Сейшельские острова | 45 692 | 0 | 168 | 0 |
| Бруней | 215 283 | 0 | 225 | 0 |
| Сент-Люсия | 28 341 | 0 | 388 | 0 |
| Антигуа и Барбуда | 8 820 | 0 | 144 | 0 |
| Сент-Винсент и Гренадины | 9 404 | 0 | 115 | 0 |
| Доминика | 14 852 | 0 | 68 | 0 |
| Фиджи | 67 925 | 0 | 875 | 0 |
| Гренада | 18 895 | 26 | 234 | 0 |
| Восточный Тимор | 23 074 | 0 | 135 | 0 |
| Лаос | 212 323 | 109 | 757 | 0 |
| Сент-Китс и Невис | 6 485 | 0 | 46 | 0 |
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Симптомы коронавируса
Инкубационный период длится около 11 дней. Симптоматика заболевания очень схожа с обычной простудой и респираторными инфекциями. Основными признаками наличия заражения коронавирусом могут быть:
Если у вас легкие симптомы, то оставайтесь дома. Чтобы облегчить состояние:
При ухудшении общего состояния необходимо немедленно обратиться к врачу.
Меры профилактики
Рекомендуемые действия для предупреждения болезни:
Горячая линия по коронавирусу в России
Если почувствовали себя плохо, то звоните в Единый консультационный центр Роспотребнадзора. Телефон: 8 (800) 200-01-12
Cайт Coronavirus Monitorus RU © 2019-2022 г. Онлайн карта распространения коронавируса. Cтатистика заражения, графики по городам России и странам мира. Источники данных: Яндекс, WHO, ECDC.
Академик Покровский назвал особенности новых типов коронавируса
Быстрее, легче, слабее
Кроме повышенной способности заражать, а значит, быстрее распространяться, подварианты «Омикрона» отличаются укороченным инкубационным периодом — симптомы могут проявиться уже через день-два после контакта с инфицированным, тогда как раньше до появления симптомов проходило в среднем 5 дней. Какие еще особенности у новых подвидов коронавируса, узнал «МК».
Фото: АГН «Москва»
Кроме повышенной способности заражать, а значит, быстрее распространяться, подварианты «Омикрона» отличаются укороченным инкубационным периодом — симптомы могут проявиться уже через день-два после контакта с инфицированным, тогда как раньше до появления симптомов проходило в среднем 5 дней. Какие еще особенности у новых подвидов коронавируса, узнал «МК».
В Интернете россияне делятся, как переносят новые варианты «короны»: кто-то сваливается в постель с 40-градусной температурой на несколько дней, кто-то ползает по стенке из-за внезапной слабости, а кто-то переносит заразу на ногах. Последних большинство. Отличить ковид от сезонных ОРВИ только по симптомам становится все сложнее.
Как и остановить его распространение: россияне вспоминают привычные допандемийные времена, поэтому нередко при проявлении симптомов простуды не изолируются дома, а накачиваются жаропонижающими и несут вирус в коллективы: «Я как минимум 4 дня ходила на аспирине и заражала всех, включая преподавателя английского, парикмахера и коллег, уверенная, что я просто простыла на кондиционерах. А потом сдала тест, а он положительный, — рассказывает Ольга, окончившая медвуз. — Первые дни очень сильно болело горло, почти сразу присоединиись ларинготрахеит (охрипший голос и кашель с мокротой), слабость и потливость. И постоянно лихорадило. Начиная с третьего дня боль в горле и болезненность в области корней зубов сошли на нет, но начался астенический синдром с выраженной слабостью, утомлением, головокружением, головной болью и сонливостью. Буквально прилегла на диван отдохнуть — и тут же провалилась в сон на час-два. И так три раза в день. Ночной сон удлинился до 10 часов. Бесконечные слабость, усталость и головокружения у меня длились неделю. Но каждый день протекал легче предыдущего.
И наконец сегодня, на 11-е сутки, меня почти полностью отпустило. Муж заболел буквально через два дня после меня. А еще через день слегли обе дочки: старшая плохо себя чувствовала всего два дня, у нее тоже была астения. А младшая пять дней лежала с температурой, и сильной, все время жаловалась на головокружения и головные боли. Все эти симптомы говорят о том, что вирус хорошо бьет по ЦНС. Словом, вариант «Омикрон» воспринимается как менее вирулентный и легче переносимый для тех, кто вакцинировался (мы все вакцинированы), и это вполне сравнимая с каким-нибудь парагриппом или аденовирусом инфекция. Астенический синдром выражен как после настоящего гриппа, даже сильнее».
«Инфекция похожа на ОРВИ, хотя очень выраженная слабость, мощная ночная потливость, как это было при других вариантах коронавируса, — отличительная черта», — говорит терапевт Алексей Водовозов.
Более короткий инкубационный период — одно из основных отличий подвидов «Омикрона». Как рассказал «МК» известный российский эпидемиолог, профессор, академик РАН Вадим Покровский, длительность инкубационного периода не связана с вирусом напрямую: «Возможно, это связано с меньшей дозой, необходимой для заражения, и более быстрой реакцией организма, которая тоже может влиять. Ведь если организм знаком с вирусом, он быстрее реагирует на встречу с ним. Нельзя исключать и генетический фактор. Сегодня мы видим людей, которые вообще не заражаются коронавирусом (после контактов с больными вирус у них не определяется) или просто не болеют. Ну а инкубационный период может быть разным и может зависеть от полученной дозы и от того, насколько организм реагирует на вирус».
Как продолжает академик Покровский, линии SARS-CoV2 отличаются небольшими изменениями в структуре белков, в частности, того белка, шипы которого присоединяются к клеткам человека:
— Поэтому у вариантов может быть разная способность присоединяться к разным клеткам — одни лучше «цепляются» к клеткам верхних дыхательных путей, другие — к легочной ткани. Важна их способность присоединяться к клеткам сосудов и внутренних органов — такие варианты чаще вызывают тяжелые формы инфекции. Сейчас варианты имеют большое разнообразие свойств, они очень перемешаны, и когда появляется новый штамм, трудно предсказать, как он себя поведет. Но можно судить по сходству с тем или иным предшествующим штаммом, и если оно есть, больше вероятность, что у людей сформировался иммунитет.
Сейчас большинство людей или переболели, или иммунизированы, то есть знакомы с разными вариантами коронавируса, — это один из факторов, обуславливающих более легкое течение. «Омикрон» и его варианты менее способны поражать клетки внутренних органов и сосудов, что позволяет надеяться, что новый подъем заболеваемости не будет таким тяжелым. Но тем не менее вакцинироваться надо, чтобы стимулировать иммунитет. И, конечно, надо носить макси — даже если они не полностью защитят, то снизят дозу получаемого вируса, что тоже влияет на течение. Пока Роспотребнадор не считает нужным внедрять обязательные карантинные меры, но не исключено, что с 1 сентября их надо будет ввести: люди съедутся из разных мест, и вероятность, что они привезут что-то новое, высока».
. ВОЗ уже призвала страны вернуть масочный режим и обязательное тестирование, от чего отказались многие правительства из-за успешно проведенных кампаний по вакцинации. В ВОЗ отмечают, что подварианты «Омикрона» продолжают вызывать новые волны госпитализаций и смертей по всему миру.