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 late January, 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.
United States Data
Data on cumulative coronavirus cases and deaths can be found in two files for states and counties.
Each row of data reports cumulative counts based on our best reporting up to the moment we publish an update. We do our best to revise earlier entries in the data when we receive new information.
Both files contain FIPS codes, a standard geographic identifier, to make it easier for an analyst to combine this data with other data sets like a map file or population data.
State-Level Data
State-level data can be found in the us-states.csv file.
date,state,fips,cases,deaths
2020-01-21,Washington,53,1,0
...
County-Level Data
County-level data can be found in the us-counties.csv file.
date,county,state,fips,cases,deaths
2020-01-21,Snohomish,Washington,53061,1,0
...
In some cases, the geographies where cases are reported do not map to standard county boundaries. See the list of geographic exceptions for more detail on these.
Github Repository
This dataset contains COVID-19 data for the United States of America made available by The New York Times on github at https://github.com/nytimes/covid-19-data
Updated
June 5, 2020
| Dataset date: Jan 24, 2020-Jun 5, 2020
This dataset updates: Every day
This dataset contains the number of confirmed cases, recoveries and deaths by country and subnational region due to the Coronavirus pandemic in Europe.
Since the outbreak of the COVID-19 crisis, the Joint Research Centre (JRC) has been supporting the European Commission in multidisciplinary areas to understand the COVID-19 emergency, anticipate its impacts, and support contingency planning.
This data provides an overview of the monitoring in the area of the 34 UCPM Participating States plus Switzerland related to sub-national data (admin level 1) on numbers of contagious and fatalities by COVID-19, collected directly from the National Authoritative sources (National monitoring websites, when available).
The sub-national granularity of the data allows to have a fit-for-purpose model to early capture the local spread and response to the COVID-19 outbreak.
The data is maintained on the JRC COVID-19 Github Repository
Updated
Live
| Dataset date: Jan 9, 2005-May 17, 2020
This dataset updates: Live
This dataset contains excess mortality data for the period covering the 2020 Covid-19 pandemic.
The data contains the excess mortality data for all known jurisdictions which publish all-cause mortality data meeting the following criteria:
daily, weekly or monthly level of granularity
includes equivalent historical data for at least one full year before 2020, and preferably at least five years (2015-2019)
includes data up to at least April 1, 2020
Most countries publish mortality data with a longer periodicity (typically quarterly or even annually), a longer publication lag time, or both. This sort of data is not suitable for ongoing analysis during an epidemic and is therefore not included here.
"Excess mortality" refers to the difference between deaths from all causes during the pandemic and the historic seasonal average. For many of the jurisdictions shown here, this figure is higher than the official Covid-19 fatalities that are published by national governments each day. While not all of these deaths are necessarily attributable to the disease, it does leave a number of unexplained deaths that suggests that the official figures of deaths attributed may significant undercounts of the pandemic's impact.
This dataset contains the number of suspected cases, confirmed cases, and deaths by Département due to the Coronavirus pandemic in Haiti. Released by the Ministry of Public Health and Population of Haiti.
Updated
May 27, 2020
| Dataset date: Jan 1, 2017-Apr 30, 2020
This dataset updates: Every month
This page provides the data published in the Education in Danger Monthly News Brief.
All data contains incidents identified in open sources. Categorized by country and with link to the relevant Monthly News Brief (where possible).
This dataset contains simulation based estimates for COVID-19 epidemic scenarios in OCHA HRP countries. Simulation is done by London School of Hygiene & Tropical Medicine(LSHTM).
Updated
April 12, 2020
| Dataset date: Jan 1, 2013-Dec 31, 2013
This dataset updates: Never
1) Natural disaster events include avalanches, extreme winter conditions, flooding, heavy rainfall, landslides & mudflows, and extreme weather (sandstorms, hail, wind, etc) as recorded by OCHA field offices and IOM Afghanistan Humanitarian Assistance Database (HADB).
2) A natural disaster incident is defined as an event that has affected (i.e. impacted) Afghans, who may or may not require humanitarian assistance.
3) HADB information is used as a main reference and supplemented by OCHA Field Office reports for those incidents where information is not available from the HADB. OCHA information includes assessment figures from OCHA, ANDMA, Red Crescent Societies, national NGOs, international NGOs, and ERM.
Updated
Live
| Dataset date: Jan 22, 2020-Jun 4, 2020
This dataset updates: Live
Novel Corona Virus (COVID-19) epidemiological data since 22 January 2020. The data is compiled by the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) from various sources including the World Health Organization (WHO), DXY.cn, BNO News, National Health Commission of the People’s Republic of China (NHC), China CDC (CCDC), Hong Kong Department of Health, Macau Government, Taiwan CDC, US CDC, Government of Canada, Australia Government Department of Health, European Centre for Disease Prevention and Control (ECDC), Ministry of Health Singapore (MOH), and others. JHU CCSE maintains the data on the 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository on Github.
Fields available in the data include Province/State, Country/Region, Last Update, Confirmed, Suspected, Recovered, Deaths.
On 23/03/2020, a new data structure was released. The current resources for the latest time series data are:
time_series_covid19_confirmed_global.csv
time_series_covid19_deaths_global.csv
time_series_covid19_recovered_global.csv
---DEPRECATION WARNING---
The resources below ceased being updated on 22/03/2020 and were removed on 26/03/2020:
time_series_19-covid-Confirmed.csv
time_series_19-covid-Deaths.csv
time_series_19-covid-Recovered.csv
Updated
Live
| Dataset date: Dec 31, 2019-Jun 5, 2020
This dataset updates: Live
Data collected by the European Centre for Disease Prevention and Control. The downloadable data file is updated daily and contains the latest available public data on COVID-19. Public-use data files allows users to manipulate the data in a format appropriate for their analyses. Users of ECDC public-use data files must comply with data use restrictions to ensure that the information will be used solely for statistical analysis or reporting purposes. For further information, visit https://www.ecdc.europa.eu/en/novel-coronavirus-china.
Updated
March 25, 2020
| Dataset date: Mar 24, 2020
This dataset updates: Every month
This set includes data on fatalities in UN peacekeeping operations. It includes a unique casualty identifier, the incident date, the mission acronym, the type of casualty, the ISO code associated with the country of origin of the personnel, the relevant M49 DESA code, the type pf personnel involved, and the type of incident.
