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Global Covid-19 Data

Global Covid-19 data on cases, deaths, vaccinations, and more

By Valtteri Kurkela [source]

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About this dataset

> > The dataset is constantly updated and synced hourly to ensure up-to-date information. With over several columns available for analysis and exploration purposes, users can extract valuable insights from this extensive dataset. > > Some of the key metrics covered in the dataset include: > > 1. Vaccinations: The dataset covers total vaccinations administered worldwide as well as breakdowns of people vaccinated per hundred people and fully vaccinated individuals per hundred people. > > 2. Testing & Positivity: Information on total tests conducted along with new tests conducted per thousand people is provided. Additionally, details on positive rate (percentage of positive Covid-19 tests out of all conducted) are included. > > 3. Hospital & ICU: Data on ICU patients and hospital patients are available along with corresponding figures normalized per million people. Weekly admissions to intensive care units and hospitals are also provided. > > 4. Confirmed Cases: The number of confirmed Covid-19 cases globally is captured in both absolute numbers as well as normalized values representing cases per million people. > > 5.Confirmed Deaths: Total confirmed deaths due to Covid-19 worldwide are provided with figures adjusted for population size (total deaths per million). > > 6.Reproduction Rate: The estimated reproduction rate (R) indicates the contagiousness of the virus within a particular country or region. > > 7.Policy Responses: Besides healthcare-related metrics, this comprehensive dataset includes policy responses implemented by countries or regions such as lockdown measures or travel restrictions. > > 8.Other Variables of InterestThe data encompasses various socioeconomic factors that may influence Covid-19 outcomes including population density,membership in a continent,gross domestic product(GDP)per capita; > > For demographic factors: > -Age Structure : percentage populations aged 65 and older,aged (70)older,median age > -Gender-specific factors: Percentage of female smokers > -Lifestyle-related factors: Diabetes prevalence rate and extreme poverty rate > > 9. Excess Mortality: The dataset further provides insights into excess mortality rates, indicating the percentage increase in deaths above the expected number based on historical data. > > The dataset consists of numerous columns providing specific information for analysis, such as ISO code for countries/regions, location names,and units of measurement for different parameters. > > Overall,this dataset serves as a valuable resource for researchers, analysts, and policymakers seeking to explore various aspects related to Covid-19

How to use the dataset

> > Introduction: > > - Understanding the Basic Structure: > - The dataset consists of various columns containing different data related to vaccinations, testing, hospitalization, cases, deaths, policy responses, and other key variables. > - Each row represents data for a specific country or region at a certain point in time. > > - Selecting Desired Columns: > - Identify the specific columns that are relevant to your analysis or research needs. > - Some important columns include population, total cases, total deaths, new cases per million people, and vaccination-related metrics. > > - Filtering Data: > - Use filters based on specific conditions such as date ranges or continents to focus on relevant subsets of data. > - This can help you analyze trends over time or compare data between different regions. > > - Analyzing Vaccination Metrics: > - Explore variables like total_vaccinations, people_vaccinated, and people_fully_vaccinated to assess vaccination coverage in different countries. > - Calculate metrics such as people_vaccinated_per_hundred or total_boosters_per_hundred for standardized comparisons across populations. > > - Investigating Testing Information: > - Examine columns such as total_tests, new_tests, and tests_per_case to understand testing efforts in various countries. > - Calculate rates like tests_per_case to assess testing efficiency or identify changes in testing strategies over time. > > - Exploring Hospitalization and ICU Data: > - Analyze variables like hosp_patients, icu_patients, and hospital_beds_per_thousand to understand healthcare systems' strain. > - Calculate rates like icu_patients_per_million or hosp_patients_per_million for cross-country comparisons. > > - Assessing Covid-19 Cases and Deaths: > - Analyze variables like total_cases, new_cases, total_deaths, and new_deaths to understand the impact of the pandemic. > - Calculate rates like total_cases_per_million or new_deaths_per_million for standardized comparisons across populations. > > - Correlating Other

Research Ideas

> - Analysis of Covid-19 Vaccination Progress: The dataset can be used to analyze and compare the progress of Covid-19 vaccination campaigns across different countries and regions. By examining variables such as total vaccinations, people vaccinated per hundred, and booster doses administered per hundred, it is possible to identify countries that have made significant progress in vaccinating their populations. > - Assessing the Impact of Policy Responses: The dataset includes information on policy responses implemented by governments to control the spread of Covid-19. By combining this data with variables such as new cases, hospitalizations, and deaths, researchers can analyze the effectiveness of different policies in controlling the virus. This analysis can help guide future public health decision-making. > - Investigating Socioeconomic Factors: With variables such as GDP per capita, poverty rate, diabetes prevalence, smoking rates, and access to basic handwashing facilities, the dataset provides an opportunity to study how socioeconomic factors influence disease outcomes during a pandemic. Researchers can examine correlations between these factors and variables such as new cases or deaths per million people to gain insights into vulnerable populations or areas that may require additional support or intervention

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > Data Source > >

License

> > > License: Dataset copyright by authors > - You are free to: > - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. > - Adapt - remix, transform, and build upon the material for any purpose, even commercially. > - You must: > - Give appropriate credit - Provide a link to the license, and indicate if changes were made. > - ShareAlike - You must distribute your contributions under the same license as the original. > - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: owid-covid-data.csv

Column name	Description
iso_code	The ISO code of a country or region. (String)
continent	The continent where a country or region is located. (String)
location	The name of a country or region. (String)
date	The date of the recorded data. (Date)
total_cases	The total number of confirmed Covid-19 cases. (Integer)
new_cases	The number of new confirmed Covid-19 cases on a specific date. (Integer)
new_cases_smoothed	The smoothed number of new confirmed Covid-19 cases on a specific date. (Integer)
total_deaths	The total number of confirmed Covid-19 deaths. (Integer)
new_deaths	The number of new confirmed Covid-19 deaths on a specific date. (Integer)
new_deaths_smoothed	The smoothed number of new confirmed Covid-19 deaths on a specific date. (Integer)
total_cases_per_million	The total number of confirmed Covid-19 cases per million people. (Float)
new_cases_per_million	The number of new confirmed Covid-19 cases per million people on a specific date. (Float)
new_cases_smoothed_per_million	The smoothed number of new confirmed Covid-19 cases per million people on a specific date. (Float)
total_deaths_per_million	The total number of confirmed Covid-19 deaths per million people. (Float)
new_deaths_per_million	The number of new confirmed Covid-19 deaths per million people on a specific date. (Float)
new_deaths_smoothed_per_million	The smoothed number of new confirmed Covid-19 deaths per million people on a specific date. (Float)
reproduction_rate	An estimate of how many people an infected individual will spread the virus to on average. (Float)
icu_patients	The number of Covid-19 patients in intensive care units. (Integer)
icu_patients_per_million	The number of Covid-19 patients in intensive care units per million people. (Float)
hosp_patients	The number of Covid-19 patients in hospitals. (Integer)
hosp_patients_per_million	The number of Covid-19 patients in hospitals per million people. (Float)
weekly_icu_admissions	The number of Covid-19 patients admitted to intensive care units on a weekly basis. (Integer)
weekly_icu_admissions_per_million	The number of Covid-19 patients admitted to intensive care units per million people on a weekly basis. (Float)
weekly_hosp_admissions	The number of Covid-19 patients admitted to hospitals on a weekly basis. (Integer)
weekly_hosp_admissions_per_million	The number of Covid-19 patients admitted to hospitals per million people on a weekly basis. (Float)
total_tests	The total number of Covid-19 tests conducted. (Integer)
new_tests	The number of new Covid-19 tests conducted on a specific date. (Integer)
total_tests_per_thousand	The total number of Covid-19 tests conducted per thousand people. (Float)
new_tests_per_thousand	The number of new Covid-19 tests conducted per thousand people on a specific date. (Float)
new_tests_smoothed	The smoothed number of new Covid-19 tests conducted on a specific date. (Integer)
new_tests_smoothed_per_thousand	The smoothed number of new Covid-19 tests conducted per thousand people on a specific date. (Float)
positive_rate	The percentage of positive Covid-19 tests out of the total tests conducted. (Float)
tests_per_case	The number of Covid-19 tests conducted per confirmed case. (Float)
tests_units	The unit of measurement for Covid-19 tests. (String)
total_vaccinations	The total number of Covid-19 vaccinations administered. (Integer)
people_vaccinated	The total number of people who have received at least one dose of the Covid-19 vaccine. (Integer)
people_fully_vaccinated	The total number of people who have received all recommended doses of the Covid-19 vaccine. (Integer)
total_boosters	The total number of booster doses of the Covid-19 vaccine administered. (Integer)
new_vaccinations	The number of new Covid-19 vaccinations administered on a specific date. (Integer)
new_vaccinations_smoothed	The smoothed number of new Covid-19 vaccinations administered on a specific date. (Integer)
population	The population of a country or region. (Integer)
excess_mortality_cumulative_absolute	The cumulative absolute excess mortality, which represents the number of additional deaths compared to the expected number based on historical data. (Integer)
excess_mortality_cumulative_per_million	The cumulative excess mortality per million people, which represents the number of additional deaths per million compared to the expected number based on historical data. (Float)
total_vaccinations_per_hundred	The number of people vaccinated per hundred individuals. (Float)
people_vaccinated_per_hundred	The number of people who have received at least one dose of the Covid-19 vaccine per hundred individuals. (Float)
people_fully_vaccinated_per_hundred	The number of people who have received all recommended doses of the Covid-19 vaccine per hundred individuals. (Float)
total_boosters_per_hundred	The number of booster doses of the Covid-19 vaccine administered per hundred individuals. (Float)
new_vaccinations_smoothed_per_million	The smoothed number of new Covid-19 vaccinations administered per million people on a specific date. (Float)
new_people_vaccinated_smoothed	The smoothed number of new people who have received at least one dose of the Covid-19 vaccine on a specific date. (Float)
new_people_vaccinated_smoothed_per_hundred	The smoothed number of new people who have received at least one dose of the Covid-19 vaccine per hundred individuals on a specific date. (Float)
population_density	The population density of a country or region. (Float)
median_age	The median age of the population in a country or region. (Float)
aged_65_older	The percentage of the population aged 65 or older in a country or region. (Float)
aged_70_older	The percentage of the population aged 70 or older in a country or region. (Float)
gdp_per_capita	The GDP per capita of a country or region. (Float)
extreme_poverty	The percentage of the population living in extreme poverty in a country or region. (Float)
cardiovasc_death_rate	The cardiovascular disease death rate per 100,000 people in a country or region. (Float)
diabetes_prevalence	The percentage of the population with diabetes in a country or region. (Float)
female_smokers	The percentage of female smokers in a country or region. (Float)
handwashing_facilities	The percentage of the population with access to basic handwashing facilities in a country or region. (Float)
hospital_beds_per_thousand	The number of hospital beds per thousand people in a country or region

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > If you use this dataset in your research, please credit Valtteri Kurkela.