Covid-19 Open Data

How to access and use the dataset

The COVID-19 Open Data Repository provides one of the largest aggregations of COVID-19 data available for technical users, with information uploaded daily from hundreds of sources.

Download the data into your own tools and systems to analyze the virus’s spread or decline, investigate COVID-related deaths, study the effects of different vaccines, and more in 20,000-plus locations worldwide.

Using our multi-layered visualization tool, you can also examine socioeconomic factors that may affect the virus’s spread and patient outcomes and gain insights into myriad scenarios associated with the global pandemic.

Download the data

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.

Accessing the data with different technologies

BigQuery

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

Google Colab

You can use Google Colab if you want to run your analysis without having to install anything in your computer, simply go to this page

Google Sheets

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/v3/latest/epidemiology.csv")

Google Sheets limits the quantity of data it accepts directly: only data from the latest subfolder or from individual locations can be imported automatically. For larger files, download the data and then use the Google Sheets’ “file” menu to import it from your computer.

R

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/v3/epidemiology.csv\")

Python

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/v3/epidemiology.csv")

JavaScript/TypeScript

Loading the JSON file using JavaScript or TypeScript can be done directly from the output folder. This code snippet loads the epidemiology table:

async function loadData() {
  const data = await (await fetch(`https://storage.googleapis.com/covid19-open-data/v3/latest/epidemiology.json`)).json();
  // Use the data...
}

Powershell

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/v3/latest/epidemiology.csv' | ConvertFrom-Csv | where location_key -eq 'AU' | select date,cumulative_confirmed,cumulative_deceased

Notes on time and geography

The location key is determined by its level: So, country-level data for the United States would use the location key US; state-level data for California, US_CA; and Santa Clara County data, with an FIPS code of 06085, would use US_CA_06085.

Level 3 data may be nested within levels 0 or 1. The city of San Francisco’s location key is US_CA_SFO.

Different regions might use different location codes and standards. For example, the US uses FIPS codes for level 2, while other regions might use an ISO standard or local equivalent. For information on the location you’re researching, consult the index table.

Understanding the data

The data is available in CSV and JSON formats. Files are published in Google Cloud Storage and can be served directly to JavaScript applications without a proxy to set the correct headers for CORS and content type.

To make the data easy to use, a main table contains the columns of all other tables joined by location key and date. For better performance, you may want to download the data separately and join the tables locally.

Each location has its own version of the aggregated table. To pull all the data for a specific region using a single endpoint, use the location's URL, inserting the location key as follows:

Each table has a full version and subsets that provide only the most recent day’s data. The full version is accessible at the URLs provided above. You can find the subsets by adding the word latest to the path. For example, here are some of the subsets available:

Note that the latest version contains the last non-null record for each key. All the tables listed above have a corresponding JSON version; simply replace csv with json in the URL.

To use this data alongside your own datasets, use the Index table to access the ISO 3166 / NUTS / FIPS code. Note, however, that administrative subdivisions are not consistent among regions. For intra-country reporting, for instance, some European Union countries use NUTS2; others, NUTS3; and many, ISO 3166-2.

The examples subfolder shows how to load and analyze the data for various programming environments.