covid-19-data

A repository of data on coronavirus cases and deaths in the U.S.

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Quick Overview

The nytimes/covid-19-data repository is a comprehensive collection of data related to the COVID-19 pandemic in the United States. Maintained by The New York Times, it provides up-to-date information on COVID-19 cases and deaths at various geographic levels, including national, state, and county data.

Pros

Regularly updated with the latest COVID-19 statistics
Data is available in easily accessible CSV format
Includes both cumulative and time series data
Provides detailed documentation on data sources and methodology

Cons

Limited to U.S. data only
May have occasional reporting delays or inconsistencies due to varying state reporting practices
Does not include more granular data such as demographics or testing information
Requires users to process and analyze the data themselves

Getting Started

To use the data from this repository:

Clone the repository:

git clone https://github.com/nytimes/covid-19-data.git

Navigate to the desired data file (e.g., us-states.csv for state-level data).
Use your preferred data analysis tool or programming language to read and process the CSV files.

Example using Python with pandas:

import pandas as pd

# Read the state-level data
df = pd.read_csv('us-states.csv')

# Display the first few rows
print(df.head())

# Calculate total cases and deaths
total_cases = df['cases'].sum()
total_deaths = df['deaths'].sum()

print(f"Total cases: {total_cases}")
print(f"Total deaths: {total_deaths}")

This example demonstrates how to read the state-level data, display the first few rows, and calculate total cases and deaths across all states.

Competitor Comparisons

covid-19-data

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Data on COVID-19 (coronavirus) cases, deaths, hospitalizations, tests • All countries • Updated daily by Our World in Data

Pros of covid-19-data (OWID)

Global coverage: Includes data from countries worldwide
Diverse metrics: Offers a wide range of COVID-19 related indicators
Regular updates: Maintained daily with the latest available information

Cons of covid-19-data (OWID)

Less granular: Typically provides country-level data, not state or county-level
Potential data lag: May have delays in reporting for some countries
Complex data structure: Can be more challenging to navigate for beginners

Code Comparison

covid-19-data (OWID):

import pandas as pd

df = pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv')
latest_data = df.groupby('location').last().reset_index()

covid-19-data (NYTimes):

import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv')
latest_data = df[df['date'] == df['date'].max()]

Both repositories provide valuable COVID-19 data, but they serve different purposes. The OWID dataset offers a global perspective with various metrics, while the NYTimes dataset focuses on detailed US-specific information. The code examples demonstrate how to load and process the latest data from each source using pandas.

COVID-19

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COVID-19 Italia - Monitoraggio situazione

Pros of COVID-19

Provides more detailed regional data for Italy, including province-level information
Offers data in multiple formats (CSV, JSON, XML) for easier integration
Includes additional metrics such as hospitalization rates and test positivity

Cons of COVID-19

Limited to Italian data, lacking global coverage
Updates may be less frequent compared to covid-19-data
Documentation primarily in Italian, which may be challenging for non-Italian speakers

Code Comparison

COVID-19 data format (CSV):

data,stato,codice_regione,denominazione_regione,lat,long,ricoverati_con_sintomi,terapia_intensiva,totale_ospedalizzati,isolamento_domiciliare,totale_positivi,variazione_totale_positivi,nuovi_positivi,dimessi_guariti,deceduti,totale_casi,tamponi,casi_testati,note

covid-19-data format (CSV):

date,county,state,fips,cases,deaths

The COVID-19 repository provides more detailed metrics for each entry, while covid-19-data focuses on a simpler structure with fewer columns.

covid19india-react

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Tracking the impact of COVID-19 in India

Pros of covid19india-react

Interactive and user-friendly frontend for visualizing COVID-19 data
Provides real-time updates and state-wise breakdowns for India
Includes additional features like vaccination data and testing statistics

Cons of covid19india-react

Limited to India-specific data, less global coverage
May require more frequent updates to maintain accuracy
Higher complexity due to frontend components and state management

Code Comparison

covid19india-react (React component):

const MapExplorer = ({ mapData, setHoveredRegion }) => {
  const [currentMap, setCurrentMap] = useState({});
  const [currentHoveredRegion, setCurrentHoveredRegion] = useState({});
  // ... more component logic
};

covid-19-data (CSV data):

date,state,fips,cases,deaths
2020-01-21,Washington,53,1,0
2020-01-22,Washington,53,1,0
2020-01-23,Washington,53,1,0

The covid19india-react repository focuses on providing a React-based frontend for visualizing COVID-19 data specific to India, while covid-19-data primarily offers raw data in CSV format for various regions, with a focus on the United States.

app

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COVID-19 App

Pros of app

Provides a mobile application for direct user interaction and information dissemination
Offers real-time updates and notifications about COVID-19 from an official source
Includes features like symptom checking and local health advice

Cons of app

Limited to mobile platforms, potentially excluding users without smartphones
May have a narrower scope of data compared to comprehensive datasets
Requires regular updates and maintenance to ensure app functionality

Code comparison

app (React Native):

import React from 'react';
import { View, Text, StyleSheet } from 'react-native';

const CovidInfo = () => (
  <View style={styles.container}>
    <Text>COVID-19 Information</Text>
  </View>
);

covid-19-data (Python):

import pandas as pd
import numpy as np

df = pd.read_csv('covid_data.csv')
daily_cases = df.groupby('date')['cases'].sum()

The app repository focuses on mobile app development using React Native, while covid-19-data primarily deals with data processing and analysis using Python libraries like pandas.

app is designed for end-users to access information directly, whereas covid-19-data serves as a comprehensive dataset for researchers, journalists, and developers to analyze and visualize COVID-19 data.

CoronaTracker

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Coronavirus tracker app for iOS & macOS with maps & charts

Pros of CoronaTracker

Provides a user-friendly iOS app for tracking COVID-19 data
Offers real-time updates and notifications
Includes interactive maps and charts for data visualization

Cons of CoronaTracker

Limited to iOS platform, reducing accessibility
May have less comprehensive data coverage compared to covid-19-data
Potentially higher resource usage due to app-based nature

Code Comparison

CoronaTracker (Swift):

func fetchData() {
    let url = URL(string: "https://api.example.com/covid-data")!
    URLSession.shared.dataTask(with: url) { data, response, error in
        // Process and update UI with fetched data
    }.resume()
}

covid-19-data (Python):

import pandas as pd

def load_data():
    url = "https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv"
    df = pd.read_csv(url)
    return df

The code snippets demonstrate the different approaches: CoronaTracker focuses on mobile app development with Swift, while covid-19-data uses Python for data processing and analysis. CoronaTracker's code is geared towards fetching and displaying data in real-time, while covid-19-data emphasizes data manipulation and analysis using pandas.

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README

Coronavirus (Covid-19) Data in the United States (Archived)

March 24, 2023 UPDATE: The data for daily cases and deaths will no longer be updated. The Times has updated its Covid tracking pages to use data from the federal government for cases and deaths. This GitHub repo will serve as an archive of the virus data reporting from The Times since 2020. For more information about this change, please see this story.

The primary data published here are the daily cumulative number of cases and deaths reported in each county and state across the U.S. since the beginning of the pandemic. We have also published these additional data sets:

Prisons: Cases in prisons
Colleges: Cases on college and university campuses.
Excess deaths: The elevated overall number of deaths during the pandemic.
Mask use: A July 2020 survey of how regularly people in each county wore masks.
Averages and anomalies: A set of pre-computed rolling averages of cases and deaths for ease of analysis or use in making graphics, along with a set of days with anomalous data that have been excluded from the averages.

Cumulative Cases and Deaths

[ U.S. Data (Raw CSV) | U.S. State-Level Data (Raw CSV) | U.S. County-Level Data (Raw CSV) ]

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.

We will publish regular updates to the data in this repository. For answers to common questions, see our [FAQ.] (https://www.nytimes.com/interactive/2020/us/about-coronavirus-data-maps.html)

Live and Historical Data

We are providing two sets of data with cumulative counts of coronavirus cases and deaths: one with our most current numbers for each geography and another with historical data showing the tally for each day for each geography.

The historical data files are at the top level of the directory and contain data up to, but not including the current day. The live data files are in the live/ directory.

A key difference between the historical and live files is that the numbers in the historical files are the final counts at the end of each day, while the live files have figures that may be a partial count released during the day but cannot necessarily be considered the final, end-of-day tally.

The historical and live data are released in three files, one for each of these geographic levels: U.S., states and counties.

Each row of data reports the cumulative number of coronavirus cases and deaths based on our best reporting up to the moment we publish an update. Our counts include both laboratory confirmed and probable cases using criteria that were developed by states and the federal government. Not all geographies are reporting probable cases and yet others are providing confirmed and probable as a single total. Please read here for a full discussion of this issue.

We do our best to revise earlier entries in the data when we receive new information. If a county is not listed for a date, then there were zero reported confirmed cases and deaths.

State and county 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.

Download all the data or clone this repository by clicking the green "Clone or download" button above.

Historical Data

U.S. National-Level Data

The daily number of cases and deaths nationwide, including states, U.S. territories and the District of Columbia, can be found in the us.csv file. (Raw CSV file here.)

date,cases,deaths
2020-01-21,1,0
...

State-Level Data

State-level data can be found in the us-states.csv file. (Raw CSV file here.)

date,state,fips,cases,deaths
2020-01-21,Washington,53,1,0
...

County-Level Data

County-level data can be found in files for each year, starting with the us-counties-2020.csv file. (Raw CSV file here.)

date,county,state,fips,cases,deaths
2020-01-21,Snohomish,Washington,53061,1,0
...

Because this file has grown too large, data for 2021 and 2022 are in separate files. A smaller file with only the most recent 30 days of data is also available in the us-counties-recent.csv file. (Raw CSV file here.)

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.

Live Data

The files in the live/ directory are also available at three geographic levels and contain all the fields the historical data files have, but with only data for the current day.

Because these are updated throughout the day, they can have inconsistent counts, are more likely to contain errors, and should be considered less reliable than the historical data. Different areas of the country update at different times and our data collection process can move at a different pace as well.

In the live files, the case and death fields have the following definitions:

cases: The total number of cases of Covid-19, including both confirmed and probable.
deaths: The total number of deaths from Covid-19, including both confirmed and probable.

The live data can be found in files at the U.S. level in the us.csv file, at the state level in the states.csv file, and at the county level in the counties.csv file.

Methodology and Definitions

The data is the product of dozens of journalists working across several time zones to monitor news conferences, analyze data releases and seek clarification from public officials on how they categorize cases.

It is also a response to a fragmented American public health system in which overwhelmed public servants at the state, county and territorial level have sometimes struggled to report information accurately, consistently and speedily. On several occasions, officials have corrected information hours or days after first reporting it. At times, cases have disappeared from a local government database, or officials have moved a patient first identified in one state or county to another, often with no explanation. In those instances, which have become more common as the number of cases has grown, our team has made every effort to update the data to reflect the most current, accurate information while ensuring that every known case is counted.

When the information is available, we count patients where they are being treated, not necessarily where they live.

In most instances, the process of recording cases has been straightforward. But because of the patchwork of reporting methods for this data across more than 50 state and territorial governments and hundreds of local health departments, our journalists sometimes had to make difficult interpretations about how to count and record cases.

For those reasons, our data will in some cases not exactly match with the information reported by states and counties. Those differences include these cases: When the federal government arranged flights to the United States for Americans exposed to the coronavirus in China and Japan, our team recorded those cases in the states where the patients subsequently were treated, even though local health departments generally did not. When a resident of Florida died in Los Angeles, we recorded her death as having occurred in California rather than Florida, though officials in Florida counted her case in their own records. And when officials in some states reported new cases without immediately identifying where the patients were being treated, we attempted to add information about their locations later, once it became available.

"Probable" and âConfirmed Cases and Deaths

Cases and deaths can be reported as either âconfirmedâ or âprobable.â Our total cases and deaths include both. The number of cases includes all cases, including those who have since recovered or died.

On April 5, 2020, the Council of State and Territorial Epidemiologists advised states to include both confirmed cases, based on confirmatory laboratory testing, and probable cases, based on specific criteria for testing, symptoms and exposure. The Centers for Disease Control adopted these definitions and national CDC data began including confirmed and probable cases on April 14, 2020.

Some governments continue to report only confirmed cases, while others are reporting both confirmed and probable numbers. And there is also another set of governments that is reporting the two types of numbers combined without providing a way to separate the confirmed from the probable.

The Geographic Exceptions section below has more details on specific areas. The methodology of individual states changes frequently.

Confirmed Cases

Confirmed cases are counts of individuals whose coronavirus infections were confirmed by a laboratory test and reported by a federal, state, territorial or local government agency. Only tests that detect viral RNA in a sample are considered confirmatory. These are often called molecular or RT-PCR tests.

Probable Cases

Probable cases count individuals who did not have a confirmed test but were evaluated by public health officials using criteria developed by states and the federal government and reported by a health department.

Public health officials consider laboratory, epidemiological, clinical and vital records evidence. Tests that detect antigens or antibodies are considered evidence towards a âprobableâ case, but are not sufficient on their own, according to the Council of State and Territorial Epidemiologists.

Confirmed Deaths

Confirmed deaths are individuals who have died and meet the definition for a confirmed Covid-19 case. Some states reconcile these records with death certificates to remove deaths from their count where Covid-19 is not listed as the cause of death. We follow health departments in removing non-Covid-19 deaths among confirmed cases when we have information to unambiguously know the deaths were not due to Covid-19, i.e. in cases of homicide, suicide, car crash or drug overdose.

âProbableâ Deaths

Probable deaths are deaths where Covid-19 is listed on the death certificate as the cause of death or a significant contributing condition, but where there has been no positive confirmatory laboratory test.

Deaths among probable cases tracked by a state or local health department where a death certificate has not yet been filed may also be counted as a probable death.

For more on how states count confirmed and probable deaths, see this article.

Dates

For each date, we show the cumulative number of confirmed cases and deaths as reported that day in that county or state. All cases and deaths are counted on the date they are first announced.

Each date includes all cases and deaths announced that day through midnight Eastern Time. As the West Coast and Hawaii tend to release all of their new data early enough in the day.

Declining Counts

In some cases, the number of cases or deaths for a state or county will decline. This can occur when a state or county corrects an error in the number of cases or deaths they've reported in the past, or when a state moves cases from one county to another. When we are able, we will historically revise counts for all impacted dates. In other cases, this will be reflected in a single-day drop in the number of cases or deaths.

Counties

In some instances, we report data from multiple counties or other non-county geographies as a single county. For instance, we report a single value for New York City, comprising the cases for New York, Kings, Queens, Bronx and Richmond Counties. In these instances the FIPS code field will be empty. (We may assign FIPS codes to these geographies in the future.) See the list of geographic exceptions.

Cities like St. Louis and Baltimore that are administered separately from an adjacent county of the same name are counted separately.

âUnknownâ Counties

Many state health departments choose to report cases separately when the patientâs county of residence is unknown or pending determination. In these instances, we record the county name as âUnknown.â As more information about these cases becomes available, the cumulative number of cases in âUnknownâ counties may fluctuate.

Sometimes, cases are first reported in one county and then moved to another county. As a result, the cumulative number of cases may change for a given county.

Geographic Exceptions

New York

All cases for the five boroughs of New York City (New York, Kings, Queens, Bronx and Richmond counties) are assigned to a single area called New York City. The number of deaths in New York City also includes probable deaths reported by the New York City health department. Deaths are reported by county of residence, except for certain periods described below.

We have changed the way we have counted deaths in New York State a few times in response to changes in how the state and New York City report their data. See this note for an explanation and timeline.

Kansas City, Mo.

Four counties (Cass, Clay, Jackson and Platte) overlap the municipality of Kansas City, Mo. The cases and deaths that we show for these four counties are only for the portions exclusive of Kansas City. Cases and deaths for Kansas City are reported as their own line.

Joplin, Mo.

Starting June 25, 2020, cases and deaths for Joplin are reported separately from Jasper and Newton counties. The cases and deaths reported for those counties are only for the portions exclusive of Joplin. Joplin cases and deaths previously appeared in the counts for those counties or as Unknown.

Alaska

Data for the Bristol Bay Borough and Lake and Peninsula Borough county equivalents are reported as a single "Bristol Bay plus Lake and Peninsula" area, and data for the Yakutat City and Borough and Hoonah-Angoon Census Area county equivalents are reported together as "Yakutat plus Hoonah-Angoon".

We continue to report data for the Chugach and Copper River Census Areas together as the Valdez-Cordova Census Area even though the state began to report data for them separately in Feb. 2021.

Alameda County, Calif.

Counts for Alameda County include cases and deaths from Berkeley and the Grand Princess cruise ship.

Douglas County, Neb.

Counts for Douglas County include cases brought to the state from the Diamond Princess cruise ship.

Chicago

All cases and deaths for Chicago are reported as part of Cook County.

Guam

Counts for Guam include cases reported from the USS Theodore Roosevelt.

Puerto Rico

Data for Puerto Rico's county-equivalent municipios are available starting on May 5, 2020. This data was not available at the beginning of the outbreak and so all cases and deaths were assigned to Unknown. Puerto Rico does not report deaths at the municipio level.

Probable Cases and Deaths and Sources

For details on which individual state counts include probable cases and deaths and on updating sourcing information for each state, please see the list of individual state pages linked to from our main tracking page.

License and Attribution

In general, we are making this data publicly available for broad, noncommercial public use including by medical and public health researchers, policymakers, analysts and local news media.

If you use this data, you must attribute it to âThe New York Timesâ in any publication. If you would like a more expanded description of the data, you could say âData from The New York Times, based on reports from state and local health agencies.â

For papers following APA format, we recommend the following citation: "The New York Times. (2021). Coronavirus (Covid-19) Data in the United States. Retrieved [Insert Date Here], from https://github.com/nytimes/covid-19-data."

If you use it in an online presentation, we would appreciate it if you would link to our U.S. tracking page at https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html.

If you use this data, please let us know at covid-data@nytimes.com.

See our LICENSE for the full terms of use for this data.

This license is co-extensive with the Creative Commons Attribution-NonCommercial 4.0 International license, and licensees should refer to that license (CC BY-NC) if they have questions about the scope of the license.

Contact Us

If you have questions about the data or licensing conditions, please contact us at:

covid-data@nytimes.com

Contributors

By Sarah Almukhtar, Aliza Aufrichtig, Anne Barnard, Matthew Bloch, Weiyi Cai, Julia Calderone, Keith Collins, Matthew Conlen, Lindsey Cook, Gabriel Gianordoli, Amy Harmon, Rich Harris, Adeel Hassan, Jon Huang, Danya Issawi, Danielle Ivory, K.K. Rebecca Lai, Alex Lemonides, Allison McCann, Richard A. Oppel Jr., Jugal K. Patel, Kirk Semple, Julie Walton Shaver, Anjali Singhvi, Charlie Smart, Mitch Smith, Albert Sun, Derek Watkins, Timothy Williams, Jin Wu and Karen Yourish. Reporting was contributed by Jordan Allen, Jeff Arnold, Ian Austen, Mike Baker, Ellen Barry, Samone Blair, Nicholas Bogel-Burroughs, Aurelien Breeden, Elisha Brown, Emma Bubola, Maddie Burakoff, Alyssa Burr, Christopher Calabrese, Sarah Cahalan, Zak Cassel, Robert Chiarito, Izzy ColÃ³n, Matt Craig, Yves De Jesus, Brendon Derr, Brandon DuprÃ©, Melissa Eddy, John Eligon, Timmy Facciola, Bianca Fortis, Matt Furber, Robert Gebeloff, Matthew Goldstein, Grace Gorenflo, Rebecca Griesbach, Benjamin Guggenheim, Barbara Harvey, Lauryn Higgins, Josh Holder, Jake Holland, Jon Huang, Anna Joyce, Ann Hinga Klein, Jacob LaGesse, Alex Lim, Alex Matthews, Patricia Mazzei, Jesse McKinley, Miles McKinley, K.B. Mensah, Sarah Mervosh, Jacob Meschke, Lauren Messman, Andrea Michelson, Jaylynn Moffat-Mowatt, Steven Moity, Paul Moon, Thomas Gibbons-Neff, Anahad O'Connor, Ashlyn OâHara, Alice Park, Azi Paybarah, Elian Peltier, Sean Plambeck, Laney Pope, Elisabetta Povoledo, Cierra S. Queen, Savannah Redl, Scott Reinhard, Thomas Rivas, Frances Robles, Natasha Rodriguez, Jess Ruderman, Alison Saldanha, Kai Schultz, Alex Schwartz, Emily Schwing, Libby Seline, Sarena Snider, Brandon Thorp, Alex Traub, Maura Turcotte, Tracey Tully, Lisa Waananen Jones, Amy Schoenfeld Walker, Jeremy White, Kristine White, Bonnie G. Wong, Tiffany Wong, Sameer Yasir and John Yoon. Data acquisition and additional work contributed by Will Houp, Andrew Chavez, Michael Strickland, Tiff Fehr, Miles Watkins, Josh Williams, Shelly Seroussi, Rumsey Taylor, Nina Pavlich, Carmen Cincotti, Ben Smithgall, Andrew Fischer, Rachel Shorey, Blacki Migliozzi, Alastair Coote, Steven Speicher, Hugh Mandeville, Robin Berjon, Thu Trinh, Carolyn Price, James G. Robinson, Phil Wells, Yanxing Yang, Michael Beswetherick, Michael Robles, Nikhil Baradwaj, Ariana Giorgi, Bella Virgilio, Dylan Momplaisir, Avery Dews, Bea Malsky and Ilana Marcus.

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