hospitalization

COVID-19 dataset for the world.

Dong, E., Du, H., & Gardner, L. (2020). "An interactive web-based dashboard to track COVID-19 in real time." The Lancet Infectious Diseases, 20(5), 533-534.

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. Excess mortality and the rate of the confirmed deaths is what we focus on in the sections below, but for a fuller assessment a wider perspective is useful. For this purpose we track the impact of the pandemic across our publication and we built country profiles for 207 countries to study the statistics on the coronavirus pandemic for every country in the world in depth.

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.

The complete COVID-19 dataset is a collection of the COVID-19 data maintained by Our World in Data and is updated daily. This dataset includes data on confirmed cases, deaths, hospitalisations, and testing, as well as other variables of potential interest. A full description of each of the data columns can be found on the OWID Codebook

A COVID-19 dataset that is updated daily and includes data on confirmed cases, deaths, excess mortality, policies, hospitalizations (and intensive care unit admissions), testing, and vaccinations as well as other variables of potential interest. Data is collected, aggregated, compiled, and documented by the Our World in Data team using different data sources repositories such as from John Hopkins University Center for Systems Science and Engineering (CSSE), European Centre for Disease Prevention and Control (ECDC), WHO, and country official data.

Straightforward comparison of COVID mortality before and after the pharmaceutical intervention as recovered from the site https://ourworldindata.org/explorers/coronavirus-data-explorer

This new dataset brings together official data on the extent of PCR testing over time for 92 countries. We provide a time series for the daily number of tests performed, or people tested, together with metadata describing data quality and comparability issues needed for the interpretation of the time series. The dataset is updated regularly through a combination of automated scraping and manual collection and verification, and is entirely replicable, with sources provided for each observation. In providing accessible cross-country data on testing output, it aims to facilitate the incorporation of this crucial information into epidemiological studies, as well as track a key component of countries’ responses to COVID-19.

The Our World in Data COVID vaccination data

To bring this pandemic to an end, a large share of the world needs to be immune to the virus. The safest way to achieve this is with a vaccine. Vaccines are a technology that humanity has often relied on in the past to bring down the death toll of infectious diseases.

Within less than 12 months after the beginning of the COVID-19 pandemic, several research teams rose to the challenge and developed vaccines that protect from SARS-CoV-2, the virus that causes COVID-19.

Now the challenge is to make these vaccines available to people around the world. It will be key that people in all countries — not just in rich countries — receive the required protection. To track this effort we at Our World in Data are building the international COVID-19 vaccination dataset that we make available on this page.

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.

29 variables from OWID's (Our World in Data's) Covid 19 dataset. Daily data from Jan 26, 2020 to Aug 2, 2023 for the same 12 countries as in the main CPEDB SPSS file: Canada, Denmark, France, Germany, Greece, Italy, Japan, Norway, Spain, Sweden, United Kingdom and United States.

Content

Widely available data on confirmed cases only becomes meaningful when it can be interpreted in light of how much a country is testing. This is why Our World in Data built the global database on COVID-19 testing [1]. The additional smoothing and per capita rates make different countries (somewhat) comparable.

Our World in Data also had a good overview of global cause of death two years ago [2] I shared that data as well for additional comparisons.

Acknowledgements

[1] Max Roser, Hannah Ritchie, Esteban Ortiz-Ospina and Joe Hasell (2020) - "Coronavirus Pandemic (COVID-19)". Published online at OurWorldInData.org. https://ourworldindata.org/coronavirus

[2] Hannah Ritchie (2018) - "Causes of Death". Published online at OurWorldInData.org. https://ourworldindata.org/causes-of-death

Inspiration

• First and second wave differences in Europe
• Global Forecasting for total 2020 numbers
• Covid death toll compared to main cause of death

Originally sourced from https://ourworldindata.org/coronavirus-source-data

Synced daily

Update 12/04/2020

The data sources have been updated to use JHU data:

From OWID:

​> On 30 November 2020, we changed our source for confirmed cases and deaths to the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. Our previous source for confirmed cases and deaths, the European Centre for Disease Prevention and Control (ECDC), had announced in November 2020 that it would switch from a daily to a weekly reporting schedule from December. Our World in Data therefore had to transition away from the ECDC as a source to continue to provide daily updates of confirmed cases and deaths. The data last sourced from the ECDC remains available as an archive in the ecdc folder. The format (variable names and types) of our complete COVID-19 dataset remains the same.

Introduction: Patients under immunotherapies were excluded from the pivotal trials of vaccinations against the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), and no population-level data on disease outcomes such as case fatality rates in relation to vaccination coverage exist. Our study aims to fill this gap by investigating whether CFRs in patients with immunotherapies decrease with increasing vaccination coverage in the total population.Methods: We combined aggregated open source data on COVID-19 vaccination coverage from “Our World in Data” with publicly available anonymized COVID-19 case reports from the FDA Adverse Event Reporting System to compute COVID-19 CFRs for patients under immunotherapy at different vaccination coverage levels in the total population. CFRs at different vaccination coverage levels were then compared to CFRs before vaccination campaign start.Results: While we found an overall decrease in CFRs on population level with increasing vaccination coverage, we found no decrease in people using anti-CD20 or glucocorticoids.Discussion: Risk-mitigation strategies on an individual- and population-level are thus still needed to lower the probability of fatal SARS-CoV2 infection for these vulnerable populations.

from January 2020 to the present.

It includes cases reported and deaths reported per country and worldwide for COVID-19.

The data source comes from the World Health Organization.

I will update as much as possible, but you may find the CSV file at: https://covid.ourworldindata.org/data/full_data.csv

ImportanceGovernments have introduced non-pharmaceutical interventions (NPIs) in response to the pandemic outbreak of Coronavirus disease (COVID-19). While NPIs aim at preventing fatalities related to COVID-19, the previous literature on their efficacy has focused on infections and on data of the first half of 2020. Still, findings of early NPI studies may be subject to underreporting and missing timeliness of reporting of cases. Moreover, the low variation in treatment timing during the first wave makes identification of robust treatment effects difficult.ObjectiveWe enhance the literature on the effectiveness of NPIs with respect to the period, the number of countries, and the analytical approach.Design, Setting, and ParticipantsTo circumvent problems of reporting and treatment variation, we analyse data on daily confirmed COVID-19-related deaths per capita from Our World in Data, and on 10 different NPIs from the Oxford COVID-19 Government Response Tracker (OxCGRT) for 169 countries from 1st July 2020 to 1st September 2021. To identify the causal effects of introducing NPIs on COVID-19-related fatalities, we apply the generalized synthetic control (GSC) method to each NPI, while controlling for the remaining NPIs, weather conditions, vaccinations, and NPI-residualized COVID-19 cases. This mitigates the influence of selection into treatment and allows to model flexible post-treatment trajectories.ResultsWe do not find substantial and consistent COVID-19-related fatality-reducing effects of any NPI under investigation. We see a tentative change in the trend of COVID-19-related deaths around 30 days after strict stay-at-home rules and to a slighter extent after workplace closings have been implemented. As a proof of concept, our model is able to identify a fatality-reducing effect of COVID-19 vaccinations. Furthermore, our results are robust with respect to various crucial sensitivity checks.ConclusionOur results demonstrate that many implemented NPIs may not have exerted a significant COVID-19-related fatality-reducing effect. However, NPIs might have contributed to mitigate COVID-19-related fatalities by preventing exponential growth in deaths. Moreover, vaccinations were effective in reducing COVID-19-related deaths.

'Our World in Data' is compiling COVID-19 testing data over time for many countries around the world. They are adding further data in the coming days as more details become available for other countries. In some cases figures refer to the number of tests, in other cases to the number of individuals who have been tested. Refer to documentation provided here.

The World Health Organization reported 6932591 Coronavirus Deaths since the epidemic began. In addition, countries reported 766440796 Coronavirus Cases. This dataset provides - World Coronavirus Deaths- actual values, historical data, forecast, chart, statistics, economic calendar and news.

CovidCases Data Worldwide

Data is extracted from https://ourworldindata.org/coronavirus-source-data. data set will be update everyday. Thanks to ourworldindata.

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.