This repository contains datasets of daily time-series data related to COVID-19 for 50+ countries around the world. The data is at the spatial resolution of states/provinces for most regions and at county/municipality resolution for Argentina, Brazil, Chile, Colombia, Czech Republic, Mexico, Netherlands, Peru, United Kingdom, and USA. All regions are assigned a unique location key, which resolves discrepancies between ISO / NUTS / FIPS codes, etc. The different aggregation levels are:

• 0: Country
• 1: Province, state, or local equivalent
• 2: Municipality, county, or local equivalent
• 3: Locality which may not follow strict hierarchical order, such as "city" or "nursing homes in X location"

There are multiple types of data:

• Outcome data Y(i,t), such as cases, tests, hospitalizations, deaths and recoveries, for region i and time t
• Static covariate data X(i), such as population size, health statistics, economic indicators, geographic boundaries
• Dynamic covariate data X(i,t), such as mobility, search trends, weather, and government interventions

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.

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 Google Cloud Storage 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.

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 day of data. The full version is accessible at the URL described in the table above. The subsets can be found by appending latest to the path. For example, the subsets of the main table are available at the following locations:

• Latest: https://storage.googleapis.com/covid19-open-data/v2/latest/main.csv
• Time series: https://storage.googleapis.com/covid19-open-data/v2/main.csv

Note that the latest version contains the last non-null record for each key. 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.

This dataset is part of the BigQuery Public Datasets Program, so you may use BigQuery to run SQL queries directly from the online query editor.

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/GoogleCloudPlatform/covid-19-open-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.

For information about each table, see the corresponding documentation linked above.

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.
• 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).

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.

Technical contributions to the data extraction pipeline are welcomed, take a look at the source directory for more information.

If you spot an error in the data, feel free to open an issue on this repository and we will review it.

If you do something cool with the data, for example related to visualization or analysis, please let us know!

We have carefully checked the license and attribution information on each data source included in this repository, and in many cases have contacted the data owners directly to ask how they would like to be attributed.

If you are the owner of a data source included here and would like us to remove data, add or alter an attribution, or add or alter license information, please open an issue on this repository and we will happily consider your request.

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.

See the source documentation for more technical details.

This project has been done in collaboration with FinMango, which provided great insights about the impact of the pandemic on the local economies and also helped with research and manual curation of data sources for many regions including South Africa and US states.

The following persons have made significant contributions to this project:

• Oscar Wahltinez
• Kevin Murphy
• Michael Brenner
• Matt Lee
• Anthony Erlinger
• Mayank Daswani
• Pranali Yawalkar
• Zack Ontiveros
• Ruth Alcantara
• Donny Cheung

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

@article{Wahltinez2020,
  author = "Oscar Wahltinez and Matt Lee and Anthony Erlinger and Mayank Daswani and Pranali Yawalkar and Kevin Murphy and Michael Brenner",
  year = 2020,
  title = "COVID-19 Open-Data: curating a fine-grained, global-scale data repository for SARS-CoV-2",
  note = "Work in progress",
  url = {https://github.com/GoogleCloudPlatform/covid-19-open-data},
}