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.

As of May 2, 2023, the outbreak of the coronavirus disease (COVID-19) had been confirmed in almost every country in the world. The virus had infected over 687 million people worldwide, and the number of deaths had reached almost 6.87 million. The most severely affected countries include the U.S., India, and Brazil.

COVID-19: background information COVID-19 is a novel coronavirus that had not previously been identified in humans. The first case was detected in the Hubei province of China at the end of December 2019. The virus is highly transmissible and coughing and sneezing are the most common forms of transmission, which is similar to the outbreak of the SARS coronavirus that began in 2002 and was thought to have spread via cough and sneeze droplets expelled into the air by infected persons.

Naming the coronavirus disease Coronaviruses are a group of viruses that can be transmitted between animals and people, causing illnesses that may range from the common cold to more severe respiratory syndromes. In February 2020, the International Committee on Taxonomy of Viruses and the World Health Organization announced official names for both the virus and the disease it causes: SARS-CoV-2 and COVID-19, respectively. The name of the disease is derived from the words corona, virus, and disease, while the number 19 represents the year that it emerged.

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

This dataset reports the daily reported number of the 7-day moving average rates of Deaths involving COVID-19 by vaccination status and by age group. Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak. Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool Data includes: * Date on which the death occurred * Age group * 7-day moving average of the last seven days of the death rate per 100,000 for those not fully vaccinated * 7-day moving average of the last seven days of the death rate per 100,000 for those fully vaccinated * 7-day moving average of the last seven days of the death rate per 100,000 for those vaccinated with at least one booster ##Additional notes As of June 16, all COVID-19 datasets will be updated weekly on Thursdays by 2pm. As of January 12, 2024, data from the date of January 1, 2024 onwards reflect updated population estimates. This update specifically impacts data for the 'not fully vaccinated' category. On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023. CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags. The data does not include vaccination data for people who did not provide consent for vaccination records to be entered into the provincial COVaxON system. This includes individual records as well as records from some Indigenous communities where those communities have not consented to including vaccination information in COVaxON. “Not fully vaccinated” category includes people with no vaccine and one dose of double-dose vaccine. “People with one dose of double-dose vaccine” category has a small and constantly changing number. The combination will stabilize the results. Spikes, negative numbers and other data anomalies: Due to ongoing data entry and data quality assurance activities in Case and Contact Management system (CCM) file, Public Health Units continually clean up COVID-19, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes, negative numbers and current totals being different from previously reported case and death counts. Public Health Units report cause of death in the CCM based on information available to them at the time of reporting and in accordance with definitions provided by Public Health Ontario. The medical certificate of death is the official record and the cause of death could be different. Deaths are defined per the outcome field in CCM marked as “Fatal”. Deaths in COVID-19 cases identified as unrelated to COVID-19 are not included in the Deaths involving COVID-19 reported. Rates for the most recent days are subject to reporting lags All data reflects totals from 8 p.m. the previous day. This dataset is subject to change.

The World Health Organization reported 6932591 Coronavirus Deaths since the epidemic began. In addition, countries reported 766440796 Coronavirus Cases. This dataset provides - World Coronavirus Deaths- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Coronavirus (COVID-19) Data in the United States

[ 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 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.

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

State-Level Data

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

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.

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 levels 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 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 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.

• Confirmed Cases

Confirmed cases are patients who test positive for the coronavirus. We consider a case confirmed when it is reported by a federal, state, territorial or local government agency.

• 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.

• 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.

• “Unknown” Counties

Many state health departments choose to report cases separately when the patient’s county of residence is unknown or pending determination. In these instances, we record the county name as “Unknown.” As more information about these cases becomes available, the cumulative number of cases in “Unknown” 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 City

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.

• 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 line.

• Alameda, Calif.

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

• Chicago

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

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 “The New York Times” in any publication. If you would like a more expanded description of the data, you could say “Data from The New York Times, based on reports from state and local health agencies.”

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 and indicate if you would be willing to talk to a reporter about your research.

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

Mitch Smith, Karen Yourish, Sarah Almukhtar, Keith Collins, Danielle Ivory, and Amy Harmon have been leading our U.S. data collection efforts.

Data has also been compiled by Jordan Allen, Jeff Arnold, Aliza Aufrichtig, Mike Baker, Robin Berjon, Matthew Bloch, Nicholas Bogel-Burroughs, Maddie Burakoff, Christopher Calabrese, Andrew Chavez, Robert Chiarito, Carmen Cincotti, Alastair Coote, Matt Craig, John Eligon, Tiff Fehr, Andrew Fischer, Matt Furber, Rich Harris, Lauryn Higgins, Jake Holland, Will Houp, Jon Huang, Danya Issawi, Jacob LaGesse, Hugh Mandeville, Patricia Mazzei, Allison McCann, Jesse McKinley, Miles McKinley, Sarah Mervosh, Andrea Michelson, Blacki Migliozzi, Steven Moity, Richard A. Oppel Jr., Jugal K. Patel, Nina Pavlich, Azi Paybarah, Sean Plambeck, Carrie Price, Scott Reinhard, Thomas Rivas, Michael Robles, Alison Saldanha, Alex Schwartz, Libby Seline, Shelly Seroussi, Rachel Shorey, Anjali Singhvi, Charlie Smart, Ben Smithgall, Steven Speicher, Michael Strickland, Albert Sun, Thu Trinh, Tracey Tully, Maura Turcotte, Miles Watkins, Jeremy White, Josh Williams, and Jin Wu.

Context

There's a story behind every dataset and here's your opportunity to share yours.# Coronavirus (Covid-19) Data in the United States

[ U.S. State-Level Data ([Raw

As of March 10, 2023, the state with the highest number of COVID-19 cases was California. Almost 104 million cases have been reported across the United States, with the states of California, Texas, and Florida reporting the highest numbers.

From an epidemic to a pandemic The World Health Organization declared the COVID-19 outbreak a pandemic on March 11, 2020. The term pandemic refers to multiple outbreaks of an infectious illness threatening multiple parts of the world at the same time. When the transmission is this widespread, it can no longer be traced back to the country where it originated. The number of COVID-19 cases worldwide has now reached over 669 million.

The symptoms and those who are most at risk Most people who contract the virus will suffer only mild symptoms, such as a cough, a cold, or a high temperature. However, in more severe cases, the infection can cause breathing difficulties and even pneumonia. Those at higher risk include older persons and people with pre-existing medical conditions, including diabetes, heart disease, and lung disease. People aged 85 years and older have accounted for around 27 percent of all COVID-19 deaths in the United States, although this age group makes up just two percent of the U.S. population

NOTE: This dataset has been retired and marked as historical-only. Only Chicago residents are included based on the home ZIP Code as provided by the medical provider. If a ZIP was missing or was not valid, it is displayed as "Unknown". Cases with a positive molecular (PCR) or antigen test are included in this dataset. Cases are counted based on the week the test specimen was collected. For privacy reasons, until a ZIP Code reaches five cumulative cases, both the weekly and cumulative case counts will be blank. Therefore, summing the “Cases - Weekly” column is not a reliable way to determine case totals. Deaths are those that have occurred among cases based on the week of death. For tests, each test is counted once, based on the week the test specimen was collected. Tests performed prior to 3/1/2020 are not included. Test counts include multiple tests for the same person (a change made on 10/29/2020). PCR and antigen tests reported to Chicago Department of Public Health (CDPH) through electronic lab reporting are included. Electronic lab reporting has taken time to onboard and testing availability has shifted over time, so these counts are likely an underestimate of community infection. The “Percent Tested Positive” columns are calculated by dividing the number of positive tests by the number of total tests . Because of the data limitations for the Tests columns, such as persons being tested multiple times as a requirement for employment, these percentages may vary in either direction from the actual disease prevalence in the ZIP Code. All data are provisional and subject to change. Information is updated as additional details are received. To compare ZIP Codes to Chicago Community Areas, please see http://data.cmap.illinois.gov/opendata/uploads/CKAN/NONCENSUS/ADMINISTRATIVE_POLITICAL_BOUNDARIES/CCAzip.pdf. Both ZIP Codes and Community Areas are also geographic datasets on this data portal. Data Source: Illinois National Electronic Disease Surveillance System, Cook County Medical Examiner’s Office, Illinois Vital Records, American Community Survey (2018)

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

This public use dataset has 11 data elements reflecting COVID-19 community levels for all available counties. This dataset contains the same values used to display information available at https://www.cdc.gov/coronavirus/2019-ncov/science/community-levels-county-map.html. CDC looks at the combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days — to determine the COVID-19 community level. The COVID-19 community level is determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge. Using these data, the COVID-19 community level is classified as low, medium , or high. COVID-19 Community Levels can help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals. See https://www.cdc.gov/coronavirus/2019-ncov/science/community-levels.html for more information. Visit CDC’s COVID Data Tracker County View* to learn more about the individual metrics used for CDC’s COVID-19 community level in your county. Please note that county-level data are not available for territories. Go to https://covid.cdc.gov/covid-data-tracker/#county-view. For the most accurate and up-to-date data for any county or state, visit the relevant health department website. *COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates.

Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

This archived public use dataset has 11 data elements reflecting United States COVID-19 community levels for all available counties.

The COVID-19 community levels were developed using a combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days. The COVID-19 community level was determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge.

Using these data, the COVID-19 community level was classified as low, medium, or high.

COVID-19 Community Levels were used to help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals.

For the most accurate and up-to-date data for any county or state, visit the relevant health department website. COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates.

Archived Data Notes:

This dataset was renamed from "United States COVID-19 Community Levels by County as Originally Posted" to "United States COVID-19 Community Levels by County" on March 31, 2022.

March 31, 2022: Column name for county population was changed to “county_population”. No change was made to the data points previous released.

March 31, 2022: New column, “health_service_area_population”, was added to the dataset to denote the total population in the designated Health Service Area based on 2019 Census estimate.

March 31, 2022: FIPS codes for territories American Samoa, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands were re-formatted to 5-digit numeric for records released on 3/3/2022 to be consistent with other records in the dataset.

March 31, 2022: Changes were made to the text fields in variables “county”, “state”, and “health_service_area” so the formats are consistent across releases.

March 31, 2022: The “%” sign was removed from the text field in column “covid_inpatient_bed_utilization”. No change was made to the data. As indicated in the column description, values in this column represent the percentage of staffed inpatient beds occupied by COVID-19 patients (7-day average).

March 31, 2022: Data values for columns, “county_population”, “health_service_area_number”, and “health_service_area” were backfilled for records released on 2/24/2022. These columns were added since the week of 3/3/2022, thus the values were previously missing for records released the week prior.

April 7, 2022: Updates made to data released on 3/24/2022 for Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands to correct a data mapping error.

April 21, 2022: COVID-19 Community Level (CCL) data released for counties in Nebraska for the week of April 21, 2022 have 3 counties identified in the high category and 37 in the medium category. CDC has been working with state officials to verify the data submitted, as other data systems are not providing alerts for substantial increases in disease transmission or severity in the state.

May 26, 2022: COVID-19 Community Level (CCL) data released for McCracken County, KY for the week of May 5, 2022 have been updated to correct a data processing error. McCracken County, KY should have appeared in the low community level category during the week of May 5, 2022. This correction is reflected in this update.

May 26, 2022: COVID-19 Community Level (CCL) data released for several Florida counties for the week of May 19th, 2022, have been corrected for a data processing error. Of note, Broward, Miami-Dade, Palm Beach Counties should have appeared in the high CCL category, and Osceola County should have appeared in the medium CCL category. These corrections are reflected in this update.

May 26, 2022: COVID-19 Community Level (CCL) data released for Orange County, New York for the week of May 26, 2022 displayed an erroneous case rate of zero and a CCL category of low due to a data source error. This county should have appeared in the medium CCL category.

June 2, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a data processing error. Tolland County, CT should have appeared in the medium community level category during the week of May 26, 2022. This correction is reflected in this update.

June 9, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a misspelling. The medium community level category for Tolland County, CT on the week of May 26, 2022 was misspelled as “meduim” in the data set. This correction is reflected in this update.

June 9, 2022: COVID-19 Community Level (CCL) data released for Mississippi counties for the week of June 9, 2022 should be interpreted with caution due to a reporting cadence change over the Memorial Day holiday that resulted in artificially inflated case rates in the state.

July 7, 2022: COVID-19 Community Level (CCL) data released for Rock County, Minnesota for the week of July 7, 2022 displayed an artificially low case rate and CCL category due to a data source error. This county should have appeared in the high CCL category.

July 14, 2022: COVID-19 Community Level (CCL) data released for Massachusetts counties for the week of July 14, 2022 should be interpreted with caution due to a reporting cadence change that resulted in lower than expected case rates and CCL categories in the state.

July 28, 2022: COVID-19 Community Level (CCL) data released for all Montana counties for the week of July 21, 2022 had case rates of 0 due to a reporting issue. The case rates have been corrected in this update.

July 28, 2022: COVID-19 Community Level (CCL) data released for Alaska for all weeks prior to July 21, 2022 included non-resident cases. The case rates for the time series have been corrected in this update.

July 28, 2022: A laboratory in Nevada reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate will be inflated in Clark County, NV for the week of July 28, 2022.

August 4, 2022: COVID-19 Community Level (CCL) data was updated on August 2, 2022 in error during performance testing. Data for the week of July 28, 2022 was changed during this update due to additional case and hospital data as a result of late reporting between July 28, 2022 and August 2, 2022. Since the purpose of this data set is to provide point-in-time views of COVID-19 Community Levels on Thursdays, any changes made to the data set during the August 2, 2022 update have been reverted in this update.

August 4, 2022: COVID-19 Community Level (CCL) data for the week of July 28, 2022 for 8 counties in Utah (Beaver County, Daggett County, Duchesne County, Garfield County, Iron County, Kane County, Uintah County, and Washington County) case data was missing due to data collection issues. CDC and its partners have resolved the issue and the correction is reflected in this update.

August 4, 2022: Due to a reporting cadence change, case rates for all Alabama counties will be lower than expected. As a result, the CCL levels published on August 4, 2022 should be interpreted with caution.

August 11, 2022: COVID-19 Community Level (CCL) data for the week of August 4, 2022 for South Carolina have been updated to correct a data collection error that resulted in incorrect case data. CDC and its partners have resolved the issue and the correction is reflected in this update.

August 18, 2022: COVID-19 Community Level (CCL) data for the week of August 11, 2022 for Connecticut have been updated to correct a data ingestion error that inflated the CT case rates. CDC, in collaboration with CT, has resolved the issue and the correction is reflected in this update.

August 25, 2022: A laboratory in Tennessee reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate may be inflated in many counties and the CCLs published on August 25, 2022 should be interpreted with caution.

August 25, 2022: Due to a data source error, the 7-day case rate for St. Louis County, Missouri, is reported as zero in the COVID-19 Community Level data released on August 25, 2022. Therefore, the COVID-19 Community Level for this county should be interpreted with caution.

September 1, 2022: Due to a reporting issue, case rates for all Nebraska counties will include 6 days of data instead of 7 days in the COVID-19 Community Level (CCL) data released on September 1, 2022. Therefore, the CCLs for all Nebraska counties should be interpreted with caution.

September 8, 2022: Due to a data processing error, the case rate for Philadelphia County, Pennsylvania,

As part of an ongoing partnership with the Census Bureau, the National Center for Health Statistics (NCHS) recently added questions to assess the prevalence of post-COVID-19 conditions (long COVID), on the experimental Household Pulse Survey. This 20-minute online survey was designed to complement the ability of the federal statistical system to rapidly respond and provide relevant information about the impact of the coronavirus pandemic in the U.S. Data collection began on April 23, 2020. Beginning in Phase 3.5 (on June 1, 2022), NCHS included questions about the presence of symptoms of COVID that lasted three months or longer. Phase 3.5 will continue with a two-weeks on, two-weeks off collection and dissemination approach. Estimates on this page are derived from the Household Pulse Survey and show the percentage of adults aged 18 and over who a) as a proportion of the U.S. population, the percentage of adults who EVER experienced post-COVID conditions (long COVID). These adults had COVID and had some symptoms that lasted three months or longer; b) as a proportion of adults who said they ever had COVID, the percentage who EVER experienced post-COVID conditions; c) as a proportion of the U.S. population, the percentage of adults who are CURRENTLY experiencing post-COVID conditions. These adults had COVID, had long-term symptoms, and are still experiencing symptoms; d) as a proportion of adults who said they ever had COVID, the percentage who are CURRENTLY experiencing post-COVID conditions; and e) as a proportion of the U.S. population, the percentage of adults who said they ever had COVID.

DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. COVID-19 state summary including the following metrics, including the change from the data reported the previous day: COVID-19 Cases (confirmed and probable) COVID-19 Tests Reported (molecular and antigen) Daily Test Positivity Patients Currently Hospitalized with COVID-19 COVID-19-Associated Deaths Additional notes: The cumulative count of tests reported for 1/17/2021 includes 286,103 older tests from previous dates, which had been missing from previous reports due to a data processing error. The older tests were added to the cumulative count of tests reported, but they were not included in the calculation of change from the previous reporting day or daily percent test positivity. Starting 5/10/2021, the date field will represent the date this data was updated on data.ct.gov. Previously the date the data was pulled by DPH was listed, which typically coincided with the date before the data was published on data.ct.gov. This change was made to standardize the COVID-19 data sets on data.ct.gov. Starting April 4, 2022, negative rapid antigen and rapid PCR test results for SARS-CoV-2 are no longer required to be reported to the Connecticut Department of Public Health as of April 4. Negative test results from laboratory based molecular (PCR/NAAT) results are still required to be reported as are all positive test results from both molecular (PCR/NAAT) and antigen tests.

Note: This dataset is no longer being updated due to the end of the COVID-19 Public Health Emergency.

The California Department of Public Health (CDPH) is identifying vaccination status of COVID-19 cases, hospitalizations, and deaths by analyzing the state immunization registry and registry of confirmed COVID-19 cases. Post-vaccination cases are individuals who have a positive SARS-Cov-2 molecular test (e.g. PCR) at least 14 days after they have completed their primary vaccination series.

Tracking cases of COVID-19 that occur after vaccination is important for monitoring the impact of immunization campaigns. While COVID-19 vaccines are safe and effective, some cases are still expected in persons who have been vaccinated, as no vaccine is 100% effective. For more information, please see https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/Post-Vaccine-COVID19-Cases.aspx

Post-vaccination infection data is updated monthly and includes data on cases, hospitalizations, and deaths among the unvaccinated and the vaccinated. Partially vaccinated individuals are excluded. To account for reporting and processing delays, there is at least a one-month lag in provided data (for example data published on 9/9/22 will include data through 7/31/22).

Notes:

• On September 9, 2022, the post-vaccination data has been changed to compare unvaccinated with those with at least a primary series completed for persons age 5+. These data will be updated monthly (first Thursday of the month) and include at least a one month lag.

• On February 2, 2022, the post-vaccination data has been changed to distinguish between vaccination with a primary series only versus vaccinated and boosted. The previous dataset has been uploaded as an archived table. Additionally, the lag on this data has been extended to 14 days.

• On November 29, 2021, the denominator for calculating vaccine coverage has been changed from age 16+ to age 12+ to reflect new vaccine eligibility criteria. The previous dataset based on age 16+ denominators has been uploaded as an archived table.

NOTE: This dataset has been retired and marked as historical-only. The recommended dataset to use in its place is https://data.cityofchicago.org/Health-Human-Services/COVID-19-Vaccination-Coverage-Citywide/6859-spec. COVID-19 vaccinations administered to Chicago residents based on the reported race-ethnicity and age group of the person vaccinated, as provided by the medical provider in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE). Vaccination Status Definitions: ·People with at least one vaccine dose: Number of people who have received at least one dose of any COVID-19 vaccine, including the single-dose Johnson & Johnson COVID-19 vaccine. ·People with a completed vaccine series: Number of people who have completed a primary COVID-19 vaccine series. Requirements vary depending on age and type of primary vaccine series received. ··People with an original booster dose: Number of people who have a completed vaccine series and have received at least one additional monovalent dose. This includes people who received a monovalent booster dose and immunocompromised people who received an additional primary dose of COVID-19 vaccine. Monovalent doses were created from the original strain of the virus that causes COVID-19. People with a bivalent dose: Number of people who received a bivalent (updated) dose of vaccine. Updated, bivalent doses became available in Fall 2022 and were created with the original strain of COVID-19 and newer Omicron variant strains. Weekly cumulative totals by vaccination status are shown for each combination of race-ethnicity and age group. Note that each age group has a row where race-ethnicity is "All" so care should be taken when summing rows. Vaccinations are counted based on the date on which they were administered. Weekly cumulative totals are reported from the week ending Saturday, December 19, 2020 onward (after December 15, when vaccines were first administered in Chicago) through the Saturday prior to the dataset being updated. Population counts are from the U.S. Census Bureau American Community Survey (ACS) 2019 1-year estimates. For some of the age groups by which COVID-19 vaccine has been authorized in the United States, race-ethnicity distributions were specifically reported in the ACS estimates. For others, race-ethnicity distributions were estimated by the Chicago Department of Public Health (CDPH) by weighting the available race-ethnicity distributions, using proportions of constituent age groups. Coverage percentages are calculated based on the cumulative number of people in each population subgroup (age group by race-ethnicity) who have each vaccination status as of the date, divided by the estimated number of Chicago residents in each subgroup. Actual counts may exceed population estimates and lead to >100% coverage, especially in small race-ethnicity subgroups of each age group. All coverage percentages are capped at 99%. All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects data currently known to CDPH. Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined. CDPH uses the most complete data available to estimate COVID-19 vaccination coverage among Chicagoans, but there are several limitations that impact our estimates. Data reported in I-CARE only include doses administered in Illinois and some doses administered outside of Illinois reported historically by Illinois providers. Doses administered by the federal Bureau of Prisons and Department of Defense are also not currently reported in I-CARE. The Veterans Health Administration began reporting doses in I-CARE beginning September 2022. Due to people receiving vaccinations that are not recorded in I-CARE that c

COVID-19 Cases information is reported through the Pennsylvania State Department’s National Electronic Disease Surveillance System (PA-NEDSS). As new cases are passed to the Allegheny County Health Department they are investigated by case investigators. During investigation some cases which are initially determined by the State to be in the Allegheny County jurisdiction may change, which can account for differences between publication of the files on the number of cases, deaths and tests. Additionally, information is not always reported to the State in a timely manner, delays can range from days to weeks, which can also account for discrepancies between previous and current files. Test and Case information will be updated daily. This resource contains individuals who received a COVID-19 test and individuals whom are probable cases. Every day, these records are overwritten with updates. Each row in the data reflects a person that is tested, not tests that are conducted. People that are tested more than once will have their testing and case data updated using the following rules:

1. Positive tests overwrite negative tests.
2. Polymerase chain reaction (PCR) tests overwrite antibody or antigen (AG) tests.
3. The first positive PCR test is never overwritten. Data collected from additional tests do not replace the first positive PCR test.

Note: On April 4th 2022 the Pennsylvania Department of Health no longer required labs to report negative AG tests. Therefore aggregated counts that included AG tests have been removed from the Municipality/Neighborhood files going forward. Versions of this data up to this cut-off have been retained as archived files.

Individual Test information is also updated daily. This resource contains the details and results of individual tests along with demographic information of the individual tested. Only PCR and AG tests are included. Every day, these records are overwritten with updates. This resource should be used to determine positivity rates.

The remaining datasets provide statistics on death demographics. Demographic, municipality and neighborhood information for deaths are reported on a weekly schedule and are not included with individual cases or tests. This has been done to protect the privacy and security of individuals and their families in accordance with the Health Insurance Portability and Accountability Act (HIPAA). Municipality or City of Pittsburgh Neighborhood is based off the geocoded home address of the individual tested.

Individuals whose home address is incomplete may not be in Allegheny County but whose temporary residency, work or other mitigating circumstance are determined to be in Allegheny County by the Pennsylvania Department of Health are counted as "Undefined".

Since the start of the pandemic, the ACHD has mapped every day’s COVID tests, cases, and deaths to their Allegheny County municipality and neighborhood. Tests were mapped to patient address, and if this was not available, to the provider location. This has recently resulted in apparent testing rates that exceeded the populations of various municipalities -- mostly those with healthcare providers. As this was brought to our attention, the health department and our data partners began researching and comparing methods to most accurately display the data. This has led us to leave those with missing home addresses off the map. Although these data will still appear in test, case and death counts, there will be over 20,000 fewer tests and almost 1000 fewer cases on the map. In addition to these map changes, we have identified specific health systems and laboratories that had data uploading errors that resulted in missing locations, and are working with them to correct these errors.

Due to minor discrepancies in the Municipal boundary and the City of Pittsburgh Neighborhood files individuals whose City Neighborhood cannot be identified are be counted as “Undefined (Pittsburgh)”.

On May 19, 2023, with the rescinding of the COVID-19 public health emergency, changes in data and reporting mechanisms prompted a change to an annual data sharing schedule for tests, cases, hospitalizations, and deaths. Dates for annual release are TBD. The weekly municipal counts and individual data produced before this changed are maintained as archive files.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

Announcement Beginning October 20, 2022, CDC will report and publish aggregate case and death data from jurisdictional and state partners on a weekly basis rather than daily. As a result, community transmission levels data reported on data.cdc.gov will be updated weekly on Thursdays, typically by 8 PM ET, instead of daily. This public use dataset has 7 data elements reflecting historical data for community transmission levels for all available counties. This dataset contains historical data for the county level of community transmission and includes updated data submitted by states and jurisdictions. Each day, the dataset is appended to contain the most recent day's data. This dataset includes data from January 1, 2021. Transmission level is set to low, moderate, substantial, or high using the calculation rules below. Currently, CDC provides the public with two versions of COVID-19 county-level community transmission level data: this dataset with the levels for each county from January 1, 2021 (Historical Changes dataset) and a dataset with the levels as originally posted (Originally Posted dataset), updated daily with the most recent day’s data. Methods for calculating county level of community transmission indicator The County Level of Community Transmission indicator uses two metrics: (1) total new COVID-19 cases per 100,000 persons in the last 7 days and (2) percentage of positive SARS-CoV-2 diagnostic nucleic acid amplification tests (NAAT) in the last 7 days. For each of these metrics, CDC classifies transmission values as low, moderate, substantial, or high (below and here). If the values for each of these two metrics differ (e.g., one indicates moderate and the other low), then the higher of the two should be used for decision-making. CDC core metrics of and thresholds for community transmission levels of SARS-CoV-2 Total New Case Rate Metric: "New cases per 100,000 persons in the past 7 days" is calculated by adding the number of new cases in the county (or other administrative level) in the last 7 days divided by the population in the county (or other administrative level) and multiplying by 100,000. "New cases per 100,000 persons in the past 7 days" is considered to have transmission level of Low (0-9.99); Moderate (10.00-49.99); Substantial (50.00-99.99); and High (greater than or equal to 100.00). Test Percent Positivity Metric: "Percentage of positive NAAT in the past 7 days" is calculated by dividing the number of positive tests in the county (or other administrative level) during the last 7 days by the total number of tests resulted over the last 7 days. "Percentage of positive NAAT in the past 7 days" is considered to have transmission level of Low (less than 5.00); Moderate (5.00-7.99); Substantial (8.00-9.99); and High (greater than or equal to 10.00). If the two metrics suggest different transmission levels, the higher level is selected. If one metric is missing, the other metric is used for the indicator. Transmission categories include: Low Transmission Threshold: Counties with fewer than 10 total cases per 100,000 population in the past 7 days, and a NAAT percent test positivity in the past 7 days below 5%; Moderate Transmission Threshold: Counties with 10-49 total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 5.0-7.99%; Substantial Transmission Threshold: Counties with 50-99 total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 8.0-9.99%; High Transmission Threshold: Counties with 100

Actions of Public Health France

Public Health France’s mission is to improve and protect the health of populations. During the health crisis linked to the COVID-19 outbreak, Santé publique France is responsible for monitoring and understanding the dynamics of the epidemic, anticipating the various scenarios and putting in place actions to prevent and limit the transmission of this virus on national territory.

The Tracking Information System (SI-DEP)

The new screening information system (SI-DEP), which has been in operation since 13 May 2020, is a secure platform where the results of the laboratory tests carried out by all city and hospital laboratories for SARS-COV2 are systematically recorded.

The creation of this information system is authorised for a period of 6 months from the end of the state of health emergency by application of Decree No 2020-551 of 12 May 2020 on the information systems referred to in Article 11 of Law No 2020-546 of 11 May 2020 extending the state of health emergency and supplementing its provisions.

Description of data

This dataset provides information at the departmental and regional level: — the daily and weekly incidence rate per age group; — the daily and weekly standardised incidence rate; — the sliding standardised incidence rate.

This dataset provides information at the national level: — the daily and weekly incidence rate by age group and sex; — the daily and weekly standardised incidence rate; — the sliding standardised incidence rate.

The incidence rate corresponds to the number of positive tests per 100,000 inhabitants. It shall be calculated as follows: (100000 * number of positive cases)/Population

Accuracy: — From 29/08 onwards, laboratory data indicators (SI-DEP) show rates of incidence, positivity and screening adjusted for screenings conducted at airports upon arrival of international flights. — For more information, see the methodological note available in the resources. Limits: — Only the biological tests of persons for whom the residence department could be located are shown on the maps. Persons whose department could not be traced in the SIDEP data are counted only at the whole French level. As a result, the sum of the tests indicated in the departments or regions is less than the number of tests indicated in France. — The time limit for repeating tests may exceed 9 days in some cases. The indicators are adjusted daily according to the receipt of the results.

Notable changes

Since 8 December, after verifying the quality of the reported data, all results of RT-PCR or Antigenic tests have been included in the production of national and territorial epidemiological indicators (incidence rates, positivity rates and screening rates) relevant to the monitoring of the COVID-19 outbreak. On the other hand, the epidemic is prolonging in time and screening capacities have increased, leading to an increasing frequency of people tested several times. Thus, an adjustment of the methods of splitting for patients benefiting from repeated tests and therefore the definition of the persons tested was necessary. Public Health France, in its patient-centred epidemiological approach, has therefore adapted its methods to ensure that these indicators reflect, in particular, the proportion of infected people among the population tested. These developments have no impact on the trends and interpretation of the dynamics of the epidemic, which remain the same. More precise test data (impact and positivity) are also published by Santé publique France (SI-DEP data).

This file contains COVID-19 death counts, death rates, and percent of total deaths by jurisdiction of residence. The data is grouped by different time periods including 3-month period, weekly, and total (cumulative since January 1, 2020). United States death counts and rates include the 50 states, plus the District of Columbia and New York City. New York state estimates exclude New York City. Puerto Rico is included in HHS Region 2 estimates. Deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1. Number of deaths reported in this file are the total number of COVID-19 deaths received and coded as of the date of analysis and may not represent all deaths that occurred in that period. Counts of deaths occurring before or after the reporting period are not included in the file. Data during recent periods are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more, depending on the jurisdiction and cause of death. Death counts should not be compared across states. Data timeliness varies by state. Some states report deaths on a daily basis, while other states report deaths weekly or monthly. The ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. Region 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Region 2: New Jersey, New York, New York City, Puerto Rico; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, Texas; Region 7: Iowa, Kansas, Missouri, Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming; Region 9: Arizona, California, Hawaii, Nevada; Region 10: Alaska, Idaho, Oregon, Washington. Rates were calculated using the population estimates for 2021, which are estimated as of July 1, 2021 based on the Blended Base produced by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The Blended Base consists of the blend of Vintage 2020 postcensal population estimates, 2020 Demographic Analysis Estimates, and 2020 Census PL 94-171 Redistricting File (see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/2020-2021/methods-statement-v2021.pdf). Rates are based on deaths occurring in the specified week/month and are age-adjusted to the 2000 standard population using the direct method (see https://www.cdc.gov/nchs/data/nvsr/nvsr70/nvsr70-08-508.pdf). These rates differ from annual age-adjusted rates, typically presented in NCHS publications based on a full year of data and annualized weekly/monthly age-adjusted rates which have been adjusted to allow comparison with annual rates. Annualization rates presents deaths per year per 100,000 population that would be expected in a year if the observed period specific (weekly/monthly) rate prevailed for a full year. Sub-national death counts between 1-9 are suppressed in accordance with NCHS data confidentiality standards. Rates based on death counts less than 20 are suppressed in accordance with NCHS standards of reliability as specified in NCHS Data Presentation Standards for Proportions (available from: https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf.).

This dataset provides values for CORONAVIRUS CASES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.