Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

As of May 2, 2023, the outbreak of the coronavirus disease (COVID-19) had spread to almost every country in the world, and more than 6.86 million people had died after contracting the respiratory virus. Over 1.16 million of these deaths occurred in the United States.

Waves of infections Almost every country and territory worldwide have been affected by the COVID-19 disease. At the end of 2021 the virus was once again circulating at very high rates, even in countries with relatively high vaccination rates such as the United States and Germany. As rates of new infections increased, some countries in Europe, like Germany and Austria, tightened restrictions once again, specifically targeting those who were not yet vaccinated. However, by spring 2022, rates of new infections had decreased in many countries and restrictions were once again lifted.

What are the symptoms of the virus? It can take up to 14 days for symptoms of the illness to start being noticed. The most commonly reported symptoms are a fever and a dry cough, leading to shortness of breath. The early symptoms are similar to other common viruses such as the common cold and flu. These illnesses spread more during cold months, but there is no conclusive evidence to suggest that temperature impacts the spread of the SARS-CoV-2 virus. Medical advice should be sought if you are experiencing any of these symptoms.

Ranking results of the countries based on COVID-19 status.

ABSTRACT Background : The Covid-19 pandemic associated with the SARS-CoV-2 has caused very high death tolls in many countries, while it has had less prevalence in other countries of Africa and Asia. Climate and geographic conditions, as well as other epidemiologic and demographic conditions, were a matter of debate on whether or not they could have an effect on the prevalence of Covid-19. Objective : In the present work, we sought a possible relevance of the geographic location of a given country on its Covid-19 prevalence. On the other hand, we sought a possible relation between the history of epidemiologic and demographic conditions of the populations and the prevalence of Covid-19 across four continents (America, Europe, Africa, and Asia). We also searched for a possible impact of pre-pandemic alcohol consumption in each country on the two year death tolls across the four continents. Methods : We have sought the death toll caused by Covid-19 in 39 countries and obtained the registered deaths from specialized web pages. For every country in the study, we have analysed the correlation of the Covid-19 death numbers with its geographic latitude, and its associated climate conditions, such as the mean annual temperature, the average annual sunshine hours, and the average annual UV index. We also analyzed the correlation of the Covid-19 death numbers with epidemiologic conditions such as cancer score and Alzheimer score, and with demographic parameters such as birth rate, mortality rate, fertility rate, and the percentage of people aged 65 and above. In regard to consumption habits, we searched for a possible relation between alcohol intake levels per capita and the Covid-19 death numbers in each country. Correlation factors and determination factors, as well as analyses by simple linear regression and polynomial regression, were calculated or obtained by Microsoft Exell software (2016). Results : In the present study, higher numbers of deaths related to Covid-19 pandemic were registered in many countries in Europe and America compared to other countries in Africa and Asia. The analysis by polynomial regression generated an inverted bell-shaped curve and a significant correlation between the Covid-19 death numbers and the geographic latitude of each country in our study. Higher death numbers were registered in the higher geographic latitudes of both hemispheres, while lower scores of deaths were registered in countries located around the equator line. In a bell shaped curve, the latitude levels were negatively correlated to the average annual levels (last 10 years) of temperatures, sunshine hours, and UV index of each country, with the highest scores of each climate parameter being registered around the equator line, while lower levels of temperature, sunshine hours, and UV index were registered in higher latitude countries. In addition, the linear regression analysis showed that the Covid-19 death numbers registered in the 39 countries of our study were negatively correlated with the three climate factors of our study, with the temperature as the main negatively correlated factor with Covid-19 deaths. On the other hand, cancer and Alzheimer's disease scores, as well as advanced age and alcohol intake, were positively correlated to Covid-19 deaths, and inverted bell-shaped curves were obtained when expressing the above parameters against a country’s latitude. Instead, the (birth rate/mortality rate) ratio and fertility rate were negatively correlated to Covid-19 deaths, and their values gave bell-shaped curves when expressed against a country’s latitude. Conclusion : The results of the present study prove that the climate parameters and history of epidemiologic and demographic conditions as well as nutrition habits are very correlated with Covid-19 prevalence. The results of the present study prove that low levels of temperature, sunshine hours, and UV index, as well as negative epidemiologic and demographic conditions and high scores of alcohol intake may worsen Covid-19 prevalence in many countries of the northern hemisphere, and this phenomenon could explain their high Covid-19 death tolls. Keywords : Covid-19, Coronavirus, SARS-CoV-2, climate, temperature, sunshine hours, UV index, cancer, Alzheimer disease, alcohol.

As of May 2, 2023, the outbreak of the coronavirus disease (COVID-19) had been confirmed in almost every country in the world. The virus had infected over 687 million people worldwide, and the number of deaths had reached almost 6.87 million. The most severely affected countries include the U.S., India, and Brazil.

COVID-19: background information COVID-19 is a novel coronavirus that had not previously been identified in humans. The first case was detected in the Hubei province of China at the end of December 2019. The virus is highly transmissible and coughing and sneezing are the most common forms of transmission, which is similar to the outbreak of the SARS coronavirus that began in 2002 and was thought to have spread via cough and sneeze droplets expelled into the air by infected persons.

Naming the coronavirus disease Coronaviruses are a group of viruses that can be transmitted between animals and people, causing illnesses that may range from the common cold to more severe respiratory syndromes. In February 2020, the International Committee on Taxonomy of Viruses and the World Health Organization announced official names for both the virus and the disease it causes: SARS-CoV-2 and COVID-19, respectively. The name of the disease is derived from the words corona, virus, and disease, while the number 19 represents the year that it emerged.

This dataset provides values for CORONAVIRUS CASES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Abstract

JHU Coronavirus COVID-19 Global Cases, by country

Documentation

PHS is updating the Coronavirus Global Cases dataset weekly, Monday, Wednesday and Friday from Cloud Marketplace.

This data comes from the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). This database was created in response to the Coronavirus public health emergency to track reported cases in real-time. The data include the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries, aggregated at the appropriate province or state. It was developed to enable researchers, public health authorities and the general public to track the outbreak as it unfolds. Additional information is available in the blog post.

Visual Dashboard (desktop): https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

Section 2

Included Data Sources are:

• World Health Organization (WHO): https://www.who.int/
• DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia.
• BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/
• National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml
• China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm
• Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html
• Macau Government: https://www.ssm.gov.mo/portal/
• Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0
• US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html
• Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html
• Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance
• European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases
• Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19
• Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus
• 1Point3Arces: https://coronavirus.1point3acres.com/en
• WorldoMeters: https://www.worldometers.info/coronavirus/

%3C!-- --%3E

Section 3

**Terms of Use: **

This GitHub repo and its contents herein, including all data, mapping, and analysis, copyright 2020 Johns Hopkins University, all rights reserved, is provided to the public strictly for educational and academic research purposes. The Website relies upon publicly available data from multiple sources, that do not always agree. The Johns Hopkins University hereby disclaims any and all representations and warranties with respect to the Website, including accuracy, fitness for use, and merchantability. Reliance on the Website for medical guidance or use of the Website in commerce is strictly prohibited.

Section 4

**U.S. county-level characteristics relevant to COVID-19 **

Chin, Kahn, Krieger, Buckee, Balsari and Kiang (forthcoming) show that counties differ significantly in biological, demographic and socioeconomic factors that are associated with COVID-19 vulnerability. A range of publicly available county-specific data identifying these key factors, guided by international experiences and consideration of epidemiological parameters of importance, have been combined by the authors and are available for use:

https://github.com/mkiang/county_preparedness/

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

This dataset provides values for CORONAVIRUS DEATHS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

2019 Novel Coronavirus COVID-19 (2019-nCoV) Visual Dashboard and Map:
https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

• Confirmed Cases by Country/Region/Sovereignty
• Confirmed Cases by Province/State/Dependency
• Deaths
• Recovered

Downloadable data:
https://github.com/CSSEGISandData/COVID-19

Additional Information about the Visual Dashboard:
https://systems.jhu.edu/research/public-health/ncov

During the first two year of the Covid-19 pandemic, deaths tolls differed from a country to another. In a previous research work on 39 countries, we have found that some population’s characteristics were either negatively (birth rate/mortality rate, fertility rate) or positively (cancer score, Alzheimer disease score, percent of people above 65 years old, levels of alcohol intake) correlated with Covid-19 mortality. We also found that low levels of climate factors (average annual temperature, average hours of sunshine, average annual level of UV index) were positively correlated with Covid-19 deaths numbers as well. In the present study, we have developped an anti-Covid Capacity index that takes into account all the above mentioned parameters. The polynomial analysis of the anti-Covid Capacity and its corresponding geographic latitude of each country has generated a bell-shaped curve, with a high coefficient of determination (R2= 0.78). Lower anti-Covid capacity values were recorded in countries of low and high latitudes, respectively. Instead, plotting covid-19 deaths numbers against geographic latitude levels has generated an inverted bell-shaped curve, with higher deaths numbers at low and high latitudes, respectively. The analysis by a simple linear regression has shown that Covid-19 deaths numbers were significantly (p= 2,40 x 10-9) and negatively correlated to the anti-Covid Capacity index values. Our data demonstrate that the negative prepandemic human conditions, and the low scores of both annual temperature and UV index in many countries were the key factors behind high Covid-19 mortality, and they can be expressed as a simple index of anti-Covid capacity of a country that can predict the death-associated severity of Covid-19 disease, and thus, according to a country’s geographic latitude.

As of January 13, 2023, Bulgaria had the highest rate of COVID-19 deaths among its population in Europe at 548.6 deaths per 100,000 population. Hungary had recorded 496.4 deaths from COVID-19 per 100,000. Furthermore, Russia had the highest number of confirmed COVID-19 deaths in Europe, at over 394 thousand.

Number of cases in Europe During the same period, across the whole of Europe, there have been over 270 million confirmed cases of COVID-19. France has been Europe's worst affected country with around 38.3 million cases, this translates to an incidence rate of approximately 58,945 cases per 100,000 population. Germany and Italy had approximately 37.6 million and 25.3 million cases respectively.

Current situation In March 2023, the rate of cases in Austria over the last seven days was 224 per 100,000 which was the highest in Europe. Luxembourg and Slovenia both followed with seven day rates of infections at 122 and 108 respectively.

Addressing risks and pandemics at a country level is a complex task that requires transdisciplinary approaches. The paper aims to identify groups of the European Union countries characterized by a similar COVID-19 Resilience Index (CRI). Developed in the paper CRI index reflects the countries’ COVID-19 risk and their readiness for a crisis situation, including a pandemic. Moreover, the study detects the factors that significantly differentiate the distinguished groups. According to our research, Bulgaria, Hungary, Malta, and Poland have the lowest COVID-19 Resilience Index score, with Croatia, Greece, Czechia, and Slovakia following close. At the same time, Ireland and Scandinavian countries occupy the top of the leader board, followed by Luxemburg. The Kruskal-Wallis test results indicate four COVID-19 risk indicators that significantly differentiate the countries in the first year of the COVID-19 pandemic. Among the significant factors are not only COVID-19-related factors, i.e., the changes in residential human mobility, the stringency of anti-COVID-19 policy, but also strictly environmental factors, namely pollution and material footprint. It indicates that the most critical global environmental issues might be crucial in the phase of a future pandemic. Moreover, we detect eight readiness factors that significantly differentiate the analysed country groups. Among the significant factors are the economic indicators such as GDP per capita and labour markets, the governance indicators such as Rule of Law, Access to Information, Implementation and Adaptability measures, and social indicators such as Tertiary Attainment and Research, Innovation, and Infrastructure.

This dataset provides values for CORONAVIRUS VACCINATION RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The County Health Rankings, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, measure the health of nearly all counties in the nation and rank them within states. This feature layer contains 2020 County Health Rankings data for nation, state, and county levels. The Rankings are compiled using county-level measures from a variety of national and state data sources. Some example measures are:adult smokingphysical inactivityflu vaccinationschild povertydriving alone to workTo see a full list of variables, as well as their definitions and descriptions, explore the Fields information by clicking the Data tab here in the Item Details. These measures are standardized and combined using scientifically-informed weights."By ranking the health of nearly every county in the nation, County Health Rankings & Roadmaps (CHR&R) illustrates how where we live affects how well and how long we live. CHR&R also shows what each of us can do to create healthier places to live, learn, work, and play – for everyone."Some new features of the 2020 Rankings data compared to previous versions:More race/ethnicity categories, including Asian/Pacific Islander and American Indian/Alaska NativeReliability flags that to flag an estimate as unreliable5 new variables: math scores, reading scores, juvenile arrests, suicides, and traffic volumeData Processing Notes:Slight modifications made to the source data are as follows:The string " raw value" was removed from field labels/aliases so that auto-generated legends and pop-ups would only have the measure's name, not "(measure's name) raw value" and strings such as "(%)", "rate", or "per 100,000" were added depending on the type of measure.Percentage and Prevalence fields were multiplied by 100 to make them easier to work with in the map.For demographic variables only, the word "numerator" was removed and the word "population" was added where appropriate.Fields dropped from analytic data file: yearall fields ending in "_cihigh" and "_cilow"and any variables that are not listed in the sources and years documentation.Analytic data file was then merged with state-specific ranking files so that all county rankings and subrankings are included in this layer.

Trends in Covid total deaths. The latest data for over 100 countries around the world.

Abstract

COVID-19 caused significant disruption to the global education system. A thorough analysis of recorded learning loss evidence documented since the beginning of the school closures between March 2020 and March 2022 finds even evidence of learning loss. Most studies observed increases in inequality where certain demographics of students experienced more significant learning losses than others. But there are also outliers, countries that managed to limit the amount of loss. This review consolidates all the available evidence and documents the empirical findings. Data for 41 countries is included, together with other variables related to the pandemic experience. This data is publicly available and will be updated regularly.

Geographic coverage

The data covers 41 countries.

Analysis unit

Country

Kind of data

Aggregate data [agg]

Mode of data collection

Other [oth]

Context

The INFORM COVID-19 Risk Index is a composite index that identifies: “countries at risk from health and humanitarian impacts of COVID-19 that could overwhelm current national response capacity, and therefore lead to a need for additional international assistance.

https://data.humdata.org/dataset/inform-covid-19-risk-index-version-0-1-2

Content

The INFORM COVID-19 Risk Index is primarily concerned with structural risk factors, i.e. those that existed before the outbreak. It can be used to support prioritization of preparedness and early response actions for the primary impacts of the pandemic, and identify countries where secondary impacts are likely to have the most critical humanitarian consequences.

The main scope of the INFORM COVID-19 Risk Index is global and regional risk-informed resource allocation, i.e. where comparable understanding of countries’ risk is important. It cannot predict the impacts of the pandemic in individual countries. It does not consider the mechanisms behind secondary impacts - for example how a COVID-19 outbreak could increase conflict risk.

Acknowledgements

https://data.humdata.org/dataset/inform-covid-19-risk-index-version-0-1-2

Photo by Rio Lecatompessy on Unsplash

Inspiration

Covid-19 Pandemic.

ABSTRACT Background: Following two years of the Covid-19 pandemic, thousands of deaths were registered around the world, however, death tolls differed from a country to another. A question on whether climate parameters in each country could or not affects coronavirus incidence and Covid-19 death toll is under debate. Objective: In the present work, it is aimed to check the numbers of deaths caused by Covid-19 in 39 countries of four continents (America, Europe, Africa and Asia), and to analyse their possible correlation with climate parameters in a given country, such as the mean of annual temperature, the annual average sunshine hours and the annual average UV index in each country. Methods: We have sought the deaths number caused by Covid-19 in 39 countries and have analysed its correlation degree with the mean annual temperature, the average annual sunshine hours and the average annual UV index. Correlation and determination factors were obtained by Microsoft Exell software (2016). Results: In the present study, higher numbers of deaths related to Covid-19 were registered in many countries of Europe and America compared to other countries in Africa and Asia. On the other hand, after both the first year and the second year of the pandemic, the death numbers registered in the 39 countries of our study were very negatively correlated with the three climate factors of our study, namely, annual average temperature, sunshine hours and UV index. Conclusion:The results of the present study prove that the above climate parameters may have some kind of influence on the coronavirus incidence through a yet unknown mechanism. Our data support the hypothesis that countries which have elevated annual temperatures and elevated sunshine hours may be less vulnerable to the coronavirus SARS-CoV-2 and to its associated Covid-19 disease. Countries with the above characteristics have also elevated levels of average annual UV rays that might play a key role against the spread of the coronavirus.Thus, geographical latitude and longitude of a given country could have been the key points for the outcome of virus incidence and Covid-19 spread around the globe during the past two years. The results prove that elevated levels of temperature, sunshine hours and UV index could play a protective effect against the coronavirus, although their mechanisms of action are still unknown.

This dataset provides values for CORONAVIRUS CASES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.