This repository contains datasets of daily time-series data related to COVID-19 for 30+ countries around the world. For most countries, the data is at the spatial resolution of states/provinces, although for US, UK, NL and CO, it is at the finer resolution of county/municipality. All regions are assigned a unique key, which resolves discrepancies between ISO / NUTS / FIPS codes, etc.

There are multiple types of data:

• Outcome data Y(i,t), such as cases, deaths, tests, for regions i and time t
• Static covariate data X(i), such as population size, GDP, latitude/ longitude
• Dynamic covariate data X(i,t), such as mobility, weather
• Dynamic interventional data A(i,t), such as government lockdowns

The data is drawn from multiple sources, as listed below, and stored in separate csv / json files, which can be easily merged due to the use of consistent geographic (and temporal) keys.

NOTE: The URL endpoint for the data has changed, please see the updated links below

¹ key is a unique string for the specific geographical region built from a combination of codes such as ISO 3166, NUTS, FIPS and other local equivalents.
² Refer to the data sources for specifics about each data source and the associated terms of use.
³ Datasets without a date column contain the most recently reported information for each datapoint to date.

For more information about how to use these files see the section about using the data, and for more details about each dataset see the section about understanding the data.

There are many other public COVID-19 datasets. However, we believe this dataset is unique in the way that it merges multiple global sources, at a fine spatial resolution, using a consistent set of region keys. We hope this will make it easier for researchers to use. We are also very transparent about the data sources, and the code for ingesting and merging the data is easy to understand and modify.

A simple visualization tool was built to explore the Open COVID-19 datasets, the Open COVID-19 Explorer:	If you want to see interactive charts with a unique UX, don't miss what @Mahks built using the Open COVID-19 dataset:
You can also check out the great work of @quixote79, a MapBox-powered interactive map site:	Experience clean, clear graphs with smooth animations thanks to the work of @jmullo:
Become an armchair epidemiologist with the COVID-19 timeline simulation tool built by @LeviticusMB:	Whether you want an interactive map, compare stats or look at charts, @saadmas has you covered with a COVID-19 Daily Tracking site:
Compare per-million data at Omnimodel thanks to @OmarJay1:	Look at responsive, comprehensive charts thanks to the work of @davidjohnstone:
Reproduction Live lets you track COVID-19 outbreaks in your region and visualise the spread of the virus over time:

The data is available as CSV and JSON files, which are published in Github Pages so they can be served directly to Javascript applications without the need of a proxy to set the correct headers for CORS and content type. Even if you only want the CSV files, using the URL served by Github Pages is preferred in order to avoid caching issues and potential, future breaking changes.

For the purpose of making the data as easy to use as possible, there is a main table which contains the columns of all other tables joined by key and date. However, performance-wise, it may be better to download the data separately and join the tables locally.

Each region has its own version of the main table, so you can pull all the data for a specific region using a single endpoint, the URL for each region is:

• Data for key in CSV format: https://storage.googleapis.com/covid19-open-data/v2/${key}/main.csv
• Data for key in JSON format: https://storage.googleapis.com/covid19-open-data/v2/${key}/main.json

Each table has a full version as well as subsets with only the last 30, 14, 7 and 1 days of data. The full version is accessible at the URL described in the table above. The subsets can be found by appending the number of days to the path. For example, the subsets of the main table are available at the following locations:

• Full version: https://storage.googleapis.com/covid19-open-data/v2/main.csv
• Latest: https://storage.googleapis.com/covid19-open-data/v2/latest/main.csv
• Last 7 days: https://storage.googleapis.com/covid19-open-data/v2/7/main.csv
• Last 14 days: https://storage.googleapis.com/covid19-open-data/v2/14/main.csv
• Last 30 days: https://storage.googleapis.com/covid19-open-data/v2/30/main.csv

Note that the latest version contains the last non-null record for each key, whereas all others contain the last N days of data (all of which could be null for some keys). All of the above listed tables have a corresponding JSON version; simply replace csv with json in the link.

If you are trying to use this data alongside your own datasets, then you can use the Index table to get access to the ISO 3166 / NUTS / FIPS code, although administrative subdivisions are not consistent among all reporting regions. For example, for the intra-country reporting, some EU countries use NUTS2, others NUTS3 and many ISO 3166-2 codes.

You can find several examples in the examples subfolder with code showcasing how to load and analyze the data for several programming environments. If you want the short version, here are a few snippets to get started.

You can use Google Colab if you want to run your analysis without having to install anything in your computer, simply go to this URL: https://colab.research.google.com/github/open-covid-19/data.

You can import the data directly into Google Sheets, as long as you stay within the size limits. For instance, the following formula loads the latest epidemiology data into the current sheet:

=IMPORTDATA("https://storage.googleapis.com/covid19-open-data/v2/latest/epidemiology.csv")

Note that Google Sheets has a size limitation, so only data from the latest subfolder can be imported automatically. To work around that, simply download the file and import it via the File menu.

If you prefer R, then this is all you need to do to load the epidemiology data:

data <- read.csv("https://storage.googleapis.com/covid19-open-data/v2/main.csv")

In Python, you need to have the package pandas installed to get started:

import pandas
data = pandas.read_csv("https://storage.googleapis.com/covid19-open-data/v2/main.csv")

Loading the JSON file using jQuery can be done directly from the output folder, this code snippet loads the epidemiology table into the data variable:

$.getJSON("https://storage.googleapis.com/covid19-open-data/v2/epidemiology.json", data => { ... }

You can also use Powershell to get the latest data for a country directly from the command line, for example to query the latest epidemiology data for Australia:

Invoke-WebRequest 'https://storage.googleapis.com/covid19-open-data/v2/latest/epidemiology.csv' | ConvertFrom-Csv | `
    where key -eq 'AU' | select date,total_confirmed,total_deceased,total_recovered

Make sure that you are using the URL linked at the table above and not the raw GitHub file, the latter is subject to change at any moment in non-compatible ways, and due to the configuration of GitHub's raw file server you may run into potential caching issues.

Missing values will be represented as nulls, whereas zeroes are used when a true value of zero is reported.

Flat table with records from all other tables joined by key and date. See below for information about all the different tables and columns. Tables not included in the main table are:

• WorldBank: A subset of individual indicators are added as columns to other tables instead; for example, the health table.
• WorldPop: Age and sex demographics breakdowns are normalized and added to the demographics table instead.
• By Age: Age breakdowns of epidemiology and hospitalization data are normalized and added to the by-age-normalized table instead (TABLE TO BE ADDED).
• By Sex: Sex breakdowns of epidemiology and hospitalization data are normalized and added to the by-sex-normalized table instead (TABLE TO BE ADDED).

Non-temporal data related to countries and regions. It includes keys, codes and names for each region, which is helpful for displaying purposes or when merging with other data:

Information related to the population demographics for each region:

Information related to the economic development for each region:

Information related to the COVID-19 infections for each date-region pair:

¹Values can be negative, typically indicating a correction or an adjustment in the way they were measured. For example, a case might have been incorrectly flagged as recovered one date so it will be subtracted from the following date.
²When the reporting authority makes a distinction between PCR and antibody testing, only PCR tests are reported here.
³Total count will not always amount to the sum of daily counts, because many authorities make changes to criteria for counting cases, but not always make adjustments to the data. There is also potential missing data. All of that makes the total counts drift away from the sum of all daily counts over time, which is why the cumulative values, if reported, are kept in a separate column.

Information related to the geography for each region:

Health related indicators for each region:

Note that the majority of the health indicators are only available at the country level.

Information related to patients of COVID-19 and hospitals:

*Values can be negative, typically indicating a correction or an adjustment in the way they were measured. For example, a case might have been incorrectly flagged as recovered one date so it will be subtracted from the following date.

**Total count will not always amount to the sum of daily counts, because many authorities make changes to criteria for counting cases, but not always make adjustments to the data. There is also potential missing data. All of that makes the total counts drift away from the sum of all daily counts over time, which is why the cumulative values, if reported, are kept in a separate column.

Google's Mobility Reports are presented in CSV form as mobility.csv with the following columns:

Summary of a government's response to the events, including a stringency index, collected from University of Oxford:

For more information about each field and how the overall stringency index is computed, see the Oxford COVID-19 government response tracker.

Daily weather information from nearest station reported by NOAA:

*The reported weather station refers to the nearest station which provides temperature measurements, but rainfall and snowfall may come from a different nearby weather station. In all cases, only weather stations which are at most 300km from the location coordinates are considered.

Most recent value for each indicator of the WorldBank Database.

Refer to the WorldBank documentation for more details, or refer to the worldbank_indicators.csv file for a short description of each indicator. Each column uses the indicator code as its name, and the rows are filled with the values for the corresponding key.

Note that WorldBank data is only available at the country level and it's not included in the main table. If no values are reported by WorldBank for the country since 2015, the row value will be null.

Demographics data extracted from WorldPop, estimating total number of people per region broken down by age and sex groupings.

Refer to the WorldPop documentation for more details. This data is normalized into buckets that are consistent with other tables and added into the demographics table; it is kept as a separate table to preserve access to the original data without any modification beyond aggregation by regional unit.

Epidemiology and hospitalizations data stratified by age:

Values in this table are stratified versions of the columns available in the epidemiology and hospitalizatons tables. Each row contains up to 10 distinct bins, for example: {new_deceased_age_00: 1, new_deceased_age_01: 45, ... , new_deceased_age_09: 32}.

Each row may have different bins, depending on the data source. This table tries to capture the raw data with as much fidelity as possible up to 10 bins. The range of each bin is encoded into the age_bin_${index} variable, for example: {age_bin_00: 0-9, age_bin_01: 10-19, age_bin_02: 20-29, ... , age_bin_09: 90-}.

Several things worth noting about this table:

• This table contains very sparse data, with very few combinations of regions and variables available.
• Records without a known age bin are discarded, so the sum of all ages may not necessary amount to the variable from the corresponding table.
• The upper and lower range of the range are inclusive values. For example, range 0-9 includes individuals with age zero up to (and including) 9.
• A row may have less than 10 bins, but never more than 10. For example: {age_bin_00: 0-49, age_bin_01: 50-, age_bin_02: null, ...}

Epidemiology and hospitalizations data stratified by sex:

Values in this table are stratified versions of the columns available in the epidemiology and hospitalizatons tables. Each row contains each variable with either _male or _female suffix: {new_deceased_male: 45, new_deceased_female: 32, new_tested_male: 45, new_tested_female: 32, ...}.

Several things worth noting about this table:

• This table contains very sparse data, with very few combinations of regions and variables available.
• Records without a known sex are discarded, so the sum of all ages may not necessary amount to the variable from the corresponding table.

For countries where both country-level and subregion-level data is available, the entry which has a null value for the subregion level columns in the index table indicates upper-level aggregation. For example, if a data point has values {country_code: US, subregion1_code: CA, subregion2_code: null, ...} then that record will have data aggregated at the subregion1 (i.e. state/province) level. If subregion1_codewere null, then it would be data aggregated at the country level.

Another way to tell the level of aggregation is the aggregation_level of the index table, see the schema documentation for more details about how to interpret it.

Please note that, sometimes, the country-level data and the region-level data come from different sources so adding up all region-level values may not equal exactly to the reported country-level value. See the data loading tutorial for more information.

There is also a notices.csv file which is manually updated with quirks about the data. The goal is to be able to query by key and date, to get a list of applicable notices to the requested subset.

The output data files are published under the CC BY-SA license. All data is subject to the terms of agreement individual to each data source, refer to the sources of data table for more details. All other code and assets are published under the Apache License 2.0.

All data in this repository is retrieved automatically. When possible, data is retrieved directly from the relevant authorities, like a country's ministry of health.

To update the contents of the output folder, first install the dependencies:

pip install -r requirements.txt

Then run the following script from the source folder to update all datasets:

cd src
python update.py

See the source documentation for more technical details.

If you spot an error in the data, or there's a country you would like to include, the best way to contribute to this project is by helping maintain the data on the relevant Wikipedia article. Not only can that data be parsed automatically by this project, but it will also help inform millions of others that receive their information from Wikipedia.

For code contributions, take a look at the source directory for more information.

If you do something cool with the data (e.g., visualization or analysis), please let us know!

The following persons have made significant contributions to this project:

• Oscar Wahltinez
• Kevin Murphy
• Michael Brenner
• Matt Lee
• Anthony Erlinger
• Mayank Daswani

Please use the following when citing this project as a source of data:

@article{Wahltinez2020,
  author = "Oscar Wahltinez and  Kevin Murphy and Michael Brenner and Matt Lee and Anthony Erlinger and Mayank Daswani",
  year = 2020,
  title = "COVID-Open-Data: curating a fine-grained, global-scale COVID-19 data repository",
  note = "Work in progress",
  url = {https://github.com/open-covid-19/data/blob/main/README.md}
}