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 the first reported coronavirus case in Washington State on Jan. 21, 2020, 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.

Data from the COVID Tracking Project from The Atlantic. Made available through Socrata COVID-19 Plugin via API. Data dictionary available: https://covidtracking.com/about-data/data-definitions

From the Web site: The COVID Tracking Project is a volunteer organization launched from The Atlantic and dedicated to collecting and publishing the data required to understand the COVID-19 outbreak in the United States.

This data

2019 Novel Coronavirus COVID-19 (2019-nCoV) Visual Dashboard and Map:
https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

• Confirmed Cases by Country/Region/Sovereignty
• Confirmed Cases by Province/State/Dependency
• Deaths
• Recovered

Downloadable data:
https://github.com/CSSEGISandData/COVID-19

Additional Information about the Visual Dashboard:
https://systems.jhu.edu/research/public-health/ncov

The COVID Tracking Project collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data we can collect for the novel coronavirus, SARS-CoV-2. We attempt to include positive and negative results, pending tests, and total people tested for each state or district currently reporting that data.

Testing is a crucial part of any public health response, and sharing test data is essential to understanding this outbreak. The CDC is currently not publishing complete testing data, so we’re doing our best to collect it from each state and provide it to the public. The information is patchy and inconsistent, so we’re being transparent about what we find and how we handle it—the spreadsheet includes our live comments about changing data and how we’re working with incomplete information.

From here, you can also learn about our methodology, see who makes this, and find out what information states provide and how we handle it.

Yahoo Knowledge Graph COVID-19 Datasets

Background

The Yahoo Knowledge Graph team at Verizon Media is responsible for providing critical COVID-19 data that feeds into Yahoo properties like Yahoo News, Yahoo Finance, and Yahoo Weather. The COVID-19 datasets include country, state, and county level information updated on a rolling basis, with updates occuring approximately hourly.

The COVID-19 datasets are constructed entirely from primary (government and public agency) sources with a clear attribution of the primary sources used for each geographical region. While other aggregations of COVID-19 data are already available, we believe ours to be the only open source COVID-19 dataset that is constructed entirely from primary sources with clear attribution back to those sources. Our hope is that additional transparency will enable more accurate analysis, aiding researchers who seek to understand and prevent further spread of the disease.

Released together with the COVID-19 dataset are two other open source projects:

• An API powered by Elide, which provides JSON-API and GraphQL interfaces to the COVID-19 public data
• A dashboard to explore the data

Datasets

The data is logically organized by region and time. Time is further organized into a snapshot of the latest updates received for all regions and the updates reported by regions for a given date. As the COVID-19 pandemic develops and local governments and agencies improve their ability to collect and present their data to the public, the schema will evolve. Please check back as sources frequently evolve.

We welcome data feeds or links to web pages that you would like us to crawl, extract, and merge into the overall stats. Feel free to submit an issue.

region-metadata

Provides general information about the regions covered in the dataset, such as geographical location and links to other public data sources.

by-region-[DATE]

Provides detailed case counts of COVID-19 in each region on [DATE] in local time for that region. Each entry (row) in the daily file represents a single region.

Please be aware that different sources release data at different and often unpredictable frequencies. The by-region-[DATE] numbers will be updated as sources release data for the given date for their region. In some cases, data for a given region is not released until many days after that calendar date has elapsed everywhere in the world. As a result, the same by-region-[DATE] file may show different aggregate statistics for the same date depending on when the by-region-[DATE] is accessed. Generally speaking, by-region-[DATE] data more than one week old is stable.

Announcement: Project Ended on October 15, 2021After over 18 months of collaboration between hundreds of GISCorps volunteers, Esri's Disaster Response Program, Coders Against COVID, HERE Technologies, dozens of government agencies, and hundreds of testing providers, GISCorps has decided to end our COVID-19 Testing and Vaccination Sites Data Creation Project as of October 15th, 2021. Our data will remain available for use by researchers and analysts, but it should not be considered a reliable source of current testing and vaccination site location information after October 15th. We are grateful for the support we have received by so many throughout the life of this monumental undertaking. Read more about this effort https://covid-19-giscorps.hub.arcgis.com/pages/contribute-covid-19-testing-sites-data.Item details page: https://giscorps.maps.arcgis.com/home/item.html?id=d7d10caf1cec43e0985cc90fbbcf91cbThis view is the original COVID-19 Testing Locations in the United States - public dataset. A backup copy also exists: https://giscorps.maps.arcgis.com/home/item.html?id=11fe8f374c344549815a716c8472832f. The parent hosted feature service is the same. This version is symbolized by type of test (molecular, antibody, antigen, or combinations thereof).This feature layer view contains information about COVID-19 screening and testing locations. It is made available to the public using the GISCorps COVID-19 Testing Site Locator app (https://giscorps.maps.arcgis.com/apps/webappviewer/index.html?id=2ec47819f57c40598a4eaf45bf9e0d16) and on findcovidtesting.com. All information was sourced from public information shared by health departments, local governments, and healthcare providers. The data are aggregated by GISCorps volunteers in collaboration with volunteers from Coders Against COVID and should not be considered complete or authoritative. Please contact testing sites or your local health department directly for official information and testing requirements.The objective of this application is to aggregate and facilitate the public communications of local governments, health departments, and healthcare providers with regard to testing site locations. GISCorps does not share any screening or testing site location information not previously made public or provided to us by one of those entities.Data dictionary document: https://docs.google.com/document/d/1HlFmtsT3GzibixPR_QJiGqGOuia9r-exN3i5UK8c6h4/edit?usp=sharingArcade code for popups: https://docs.google.com/document/d/1PDOq-CxUX9fuC2v3N8muuuxN5mLMinWdf7fiwUt1lOM/edit?usp=sharing

This dataset is a per-state amalgamation of demographic, public health and other relevant predictors for COVID-19.

Deaths, Infections and Tests by State

The COVID Tracking Project: https://covidtracking.com/data/api

Used positive, death and totalTestResults from the API for, respectively, Infected, Deaths and Tested in this dataset. Please read the documentation of the API for more context on those columns

Predictor Data and Sources

Population (2020)

Density is people per meter squared https://worldpopulationreview.com/states/

ICU Beds and Age 60+

https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/

GDP

https://worldpopulationreview.com/states/gdp-by-state/

Income per capita (2018)

https://worldpopulationreview.com/states/per-capita-income-by-state/

Gini

https://en.wikipedia.org/wiki/List_of_U.S._states_by_Gini_coefficient

Unemployment (2020)

Rates from Feb 2020 and are percentage of labor force
https://www.bls.gov/web/laus/laumstrk.htm

Sex (2017)

Ratio is Male / Female
https://www.kff.org/other/state-indicator/distribution-by-gender/

Smoking Percentage (2020)

https://worldpopulationreview.com/states/smoking-rates-by-state/

Influenza and Pneumonia Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/flu_pneumonia_mortality/flu_pneumonia.htm

Chronic Lower Respiratory Disease Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/lung_disease_mortality/lung_disease.htm

Active Physicians (2019)

https://www.kff.org/other/state-indicator/total-active-physicians/

Hospitals (2018)

https://www.kff.org/other/state-indicator/total-hospitals

Health spending per capita

Includes spending for all health care services and products by state of residence. Hospital spending is included and reflects the total net revenue. Costs such as insurance, administration, research, and construction expenses are not included.
https://www.kff.org/other/state-indicator/avg-annual-growth-per-capita/

Pollution (2019)

Pollution: Average exposure of the general public to particulate matter of 2.5 microns or less (PM2.5) measured in micrograms per cubic meter (3-year estimate)
https://www.americashealthrankings.org/explore/annual/measure/air/state/ALL

Medium and Large Airports

For each state, number of medium and large airports https://en.wikipedia.org/wiki/List_of_the_busiest_airports_in_the_United_States

Temperature (2019)

Note that FL was incorrect in the table, but is corrected in the Hottest States paragraph
https://worldpopulationreview.com/states/average-temperatures-by-state/
District of Columbia temperature computed as the average of Maryland and Virginia

Urbanization (2010)

Urbanization as a percentage of the population https://www.icip.iastate.edu/tables/population/urban-pct-states

Age Groups (2018)

https://www.kff.org/other/state-indicator/distribution-by-age/

School Closure Dates

Schools that haven't closed are marked NaN https://www.edweek.org/ew/section/multimedia/map-coronavirus-and-school-closures.html

Note that some datasets above did not contain data for District of Columbia, this missing data was found via Google searches manually entered.

Made available through Socrata COVID-19 Plugin via API.

From the source Web site:

This dataset is intended to be used as a baseline for understanding the typical bed capacity and average yearly bed utilization of hospitals reporting such information. The date of last update received from each hospital may be varied. While the dataset is not updated in real-time, this information is critical for understanding the impact of a high utilization event, like COVID-19.

Data source: https://coronavirus-resources.esri.com/datasets/1044bb19da8d4dbfb6a96eb1b4ebf629_0?geometry=49.394%2C-16.820%2C-74.356%2C72.123

Definitive Healthcare is the leading provider of data, intelligence, and analytics on healthcare organizations and practitioners. In this service, Definitive Healthcare provides intelligence on the numbers of licensed beds, staffed beds, ICU beds, and the bed utilization rate for the hospitals in the United States.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Context

This dataset was curated from the Bing search logs (desktop users only) over the period of Jan 1st, 2020 – (Current Month - 1). Only searches that were issued many times by multiple users were included. Dataset includes queries from all over the world that had an intent related to the Coronavirus or Covid-19. In some cases this intent is explicit in the query itself, e.g. “Coronavirus updates Seattle” in other cases it is implicit , e.g. “Shelter in place”. Implicit intent of search queries (e.g. Toilet paper) were extracted by using Random walks on the click graph approach as outlined in the following paper by Nick Craswell et al at Microsoft Research: https://www.microsoft.com/en-us/research/wp-content/uploads/2007/07/craswellszummer-random-walks-sigir07.pdf All personal data was removed. Source - https://msropendata.com/datasets/c5031874-835c-48ed-8b6d-31de2dad0654

Acknowledgements

Data Source: Bing Coronavirus Query set (https://github.com/microsoft/BingCoronavirusQuerySet)

License - Open Use of Data Agreement v1.0

Content

Inside the data folder there is a folder 2020 (for the year) which contains two kinds of files.

QueriesByCountry_DateRange.tsv : A tab separated text file that contains queries with Coronavirus intent by Country. QueriesByState_DateRange.tsv : A tab separated text file that contains queries with Coronavirus intent by State.

QueriesByCountry Date : string, Date on which the query was issued.

Query : string, The actual search query issued by user(s).

IsImplicitIntent : bool, True if query did not mention covid or coronavirus or sarsncov2 (e.g, “Shelter in place”). False otherwise.

Country : string, Country from where the query was issued.

PopularityScore : int, Value between 1 and 100 inclusive. 1 indicates least popular query on the day/Country with Coronavirus intent, and 100 indicates the most popular query for the same Country on the same day.

QueriesByState Date : string, Date on which the query was issued.

Query : string, The actual search query issued by user(s).

IsImplicitIntent : bool, True if query did not mention covid or coronavirus or sarsncov2 (e.g, “Shelter in place”). False otherwise.

State : string, State from where the query was issued.

Country :string, Country from where the query was issued.

PopularityScore : int, Value between 1 and 100 inclusive. 1 indicates least popular query on the day/State/Country with Coronavirus intent, and 100 indicates the most popular query for the same geogrpahy on the same day.