This dataset contains model-based census tract estimates. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. The dataset includes estimates for 40 measures: 12 for health outcomes, 7 for preventive services use, 4 for chronic disease-related health risk behaviors, 7 for disabilities, 3 for health status, and 7 for health-related social needs. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2020 population data, and American Community Survey 2018–2022 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. More information about the methodology can be found at www.cdc.gov/places.

This dataset contains model-based Census tract level estimates for the PLACES project by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. It represents a first-of-its kind effort to release information uniformly on this large scale. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2019 or 2018 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 or 2014–2018 estimates. The 2021 release uses 2019 BRFSS data for 22 measures and 2018 BRFSS data for 7 measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours a night). Seven measures are based on the 2018 BRFSS data because the relevant questions are only asked every other year in the BRFSS. This data only covers the health of adults (people 18 and over) in East Baton Rouge Parish. All estimates lie within a 95% confidence interval.

This dataset contains model-based census tract level estimates in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. These data can be joined with the Census tract 2022 boundary file in a GIS system to produce maps for 40 measures at the census tract level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

This dataset contains place-level (incorporated and census-designated places) social determinants of health (SDOH) measures from the American Community Survey 5-year data for the entire United States—50 states and the District of Columbia. Data were downloaded from data.census.gov using Census API and processed by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. These measures complement existing PLACES measures, including PLACES SDOH measures (e.g., health insurance, routine check-up). These data can be used together with PLACES data to identify which health and SDOH issues overlap in a community to help inform public health planning. To access spatial data, please use the ArcGIS Online service: https://cdcarcgis.maps.arcgis.com/home/item.html?id=d51009ea78b54635be95c6ec9955ec17.

This dataset contains census tract-level non-medical factor measures from the American Community Survey 5-year data for the entire United States—50 states and the District of Columbia. Data were downloaded from data.census.gov using Census API and processed by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. These measures complement existing PLACES measures, including PLACES non-medical factor measures (e.g., health insurance, routine check-up). These data can be used together with PLACES data to identify which health and non-medical factor issues overlap in a community to help inform public health planning.

To access spatial data, please use the ArcGIS Online service: https://cdcarcgis.maps.arcgis.com/home/item.html?id=d51009ea78b54635be95c6ec9955ec17.

Mapping Layer Data Released: 06/15/2017, | Last Updated 04/20/2024Data Currency: This data is checked semi-annually from it's enterprise federal source fo 2010 CENSUS Data and will support mapping, analysis, data exports and the Open Geospatial Consortium (OGC) Application Programming Interface (API).Data Update Frequency: Twice, YearlyData Cycle | History (as required below)QA/QC Performed: December, 2024Next Scheduled Data QA/QC: July, 2024CDC PLACES (2010 CENSUS) FEATURE LAYERData Requester: Rhode Island Executive Office of Health and Human Service (OHHS) via Health Equity Institute (HEI).Data Requester: Rhode Island Department of Health, Maternal Child Health via Health Equity Institute (HEI).Data Request: Provide a database deliverable via download that contains both US CENSUS tracts and USPS Zip Code Tabulation Areas (ZCTA).HEALTH EQUITY INSTITUTE DATA CONNECT RI Using Modern GIS (Mapping)🡅 Click IT 🡅Facilitate transformative mapping visualizations that engage constituents and measure the impact of real-world solutions.Instructions to Join Your Data Provided Below STEP 1: Video (Pending)STEP 2: Video (Pending)STEP 3: Video (Pending)There are twenty-two U.S. CENSUS fields (download here) that you can join to your datasets. For additional insight, please contact the Center for Health Data and Analysis (CHDA) Rhode Island Department of Health (GIS) Mapping Department for assistance.Database Enhancement: This database contains two (2) additional data fields for consideration to be added to the existing 2020 State of Rhode Island Health Equity Map.Zip Code Tabulation Area (ZCTA)ZCTA/Tract Relationship (Singular ZCTAs per Tract, versus Multiple ZCTAs per Tract)Additional Information: While ZCTAs can be useful for certain qualitative purposes, such as broad or general high level analysis, they may not provide the level of granularity and accuracy required for in-depth demographic research which is required for policy mapping. ZCTAs can change frequently as the US Postal Service (USPS) adjusts postal routes and boundaries. These changes can lead to inconsistencies and challenges in tracking demographic trends and making accurate comparisons over time.RIDOH GIS encourages analysts to make the appropriate choice of using census based data, with their consistent boundaries readily available for suitability for spatial analysis when conducting detailed demographic research.Here are a few reasons why you might want to consider using census based data (tracts, block groups, and blocks) instead of ZCTAs:1. Inaccurate Representations: ZCTAs are not designed for statistical analysis or demographic research. They are created by the United States Postal Service (USPS) for efficient mail delivery and can often span multiple cities, counties, or even states. As a result, ZCTAs may not accurately represent the actual geographic boundaries or demographic characteristics of a specific area.2. Lack of Granularity: ZCTAs are typically larger than census tracts, which are smaller, more homogeneous geographic units defined by the U.S. Census Bureau. Census tracts are designed to be relatively consistent in terms of population size, allowing for more detailed analysis at a local level. ZCTAs, on the other hand, can vary significantly in terms of population size, making it challenging to draw precise conclusions about specific neighborhoods or communities.3. Data Availability and Compatibility: Census tracts are used by the U.S. Census Bureau to collect and report demographic data. Consequently, a wide range of demographic information, such as population counts, age distribution, income levels, and education levels, is readily available at the census tract level. In contrast, data specifically tailored to ZCTAs may be more limited, making it difficult to obtain comprehensive and consistent data for demographic analysis.4. Changes Over Time: Census tracts are relatively stable over time, allowing for consistent longitudinal analysis. ZCTAs, however, can change frequently as the USPS adjusts postal routes and boundaries. These changes can lead to inconsistencies and challenges in tracking demographic trends and making accurate comparisons over time.5. Spatial Analysis: Census tracts are designed to maintain a level of spatial proximity, adjacency, or connectedness of these data containers while providing consistency and continuity over time - making them useful for spatial analysis. Mapping. ZCTAs, on the other hand, may not exhibit the same level of spatial coherence due to their primary purpose being mail delivery efficiency rather than geographic representation.State Agencies - Contact RIDOH GIS - Learn More About Mapping Data Available at the Census Tract LevelRIDOH GIS releases this database with the caveats noted above and that the researcher can accurately align the ZCTAs with the corresponding census tracts. Careful consideration should be given to the comparability and compatibility of the data collected at different geographic levels to ensure valid and meaningful statistical conclusions. Data Dictionary: 2010 Decennial CensusOBJECT ID - the count of each census tract entity.GEOID (10) STATE,COUNTY,TRACT - Numeric US CENSUS Tract Description (2010) HEZ (10) - Health Equity Zone (2020)LOCATION (10) - Plain Language Census Tract Descriptor (2010)COUNTY (10) NAME - County Name (2010)STATE (10) NAME - State Name (2010)ZCTA (23) - Zip Code Tabulation Area - Numeric US CENSUS ZCTA Description (2023)ZCTA/TRACT CONTEXT - Number of ZCTAs (Singular/Multiple) that reside within a US CENSUS TractST (10) - Numeric US CENSUS Tract Description (2010) CO (10) - Numeric US CENSUS Tract Description (2010)ST (10) CO (10) - Numeric US CENSUS Tract Description (2010)TRACT (10) - Numeric US CENSUS Tract Description (2010)GEOID (10) - Numeric US CENSUS Tract Description (2010)TRIBAL TRACT (10) - Numeric US CENSUS Tract Description (2010)Additional Mapping DataThe user is provided authoritative Federal Information Processing Standards (FIPS) such as numeric descriptions of state, county and tract identification, in addition to shape and length measurements of each census tract for data joining purposes.STATE (10) - Federal Information Processing Standards (FIPS)COUNTY (10) - Federal Information Processing Standards (FIPS)STATE (10), COUNTY (10) - Federal Information Processing Standards (FIPS)TRACT (10) - Federal Information Processing Standards (FIPS)TRIBAL TRACT (10) - Federal Information Processing Standards (FIPS)ST ABBRV (10) - State AbbreviationShape_Length - Total length of the polygon's (census tract) perimeter, in the units used by the feature class' coordinate system.Shape_Area - Total area of the polygon's (census tract) in the units used by the feature class' coordinate system.Data Source: Series Information for 2020 Census 5-Digit ZIP Code Tabulation Area (ZCTA5) National TIGER/Line Shapefiles, Current Open Geospatial Consortium (OGC) Application Programming Interface (API) Census ZIP Code Tabulation Areas - OGC Features copy this link to embed it in OGC Compliant viewers. For more information, please visit: ZIP Code Tabulation Areas (ZCTAs)To Report Data Discrepancies Contact the Rhode Island Department of Health (RIDOH) GIS (mapping) OfficePlease Be Certain To --Provide a Brief Description of What the Discrepancy IsInclude Your, Name, Organization, Telephone NumberAttach the Complete .xlsx with the Discrepancy Highlighted

This dataset contains model-based county-level estimates in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. Project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2022 county population estimates, and American Community Survey (ACS) 2018–2022 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. These data can be joined with the census 2022 county boundary file in a GIS system to produce maps for 40 measures at the county level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

This dataset contains ZCTA-level social determinants of health (SDOH) measures from the American Community Survey 5-year data for the entire United States—50 states and the District of Columbia. Data were downloaded from data.census.gov using Census API and processed by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. These measures complement existing PLACES measures, including PLACES SDOH measures (e.g., health insurance, routine check-up). These data can be used together with PLACES data to identify which health and SDOH issues overlap in a community to help inform public health planning. To access spatial data, please use the ArcGIS Online service: https://cdcarcgis.maps.arcgis.com/home/item.html?id=d51009ea78b54635be95c6ec9955ec17.

In 1984, the Centers for Disease Control and Prevention (CDC) initiated the state-based Behavioral Risk Factor Surveillance System (BRFSS)--a cross-sectional telephone survey that state health departments conduct monthly over landline telephones and cellular telephones with a standardized questionnaire and technical and methodologic assistance from CDC. BRFSS is used to collect prevalence data among adult U.S. residents regarding their risk behaviors and preventive health practices that can affect their health status. Respondent data are forwarded to CDC to be aggregated for each state, returned with standard tabulations, and published at year's end by each state. In 2011, more than 500,000 interviews were conducted in the states, the District of Columbia, and participating U.S. territories and other geographic areas.The files in this deposit were downloaded from the CDC website by Julia Dennett, Yale University, and Toby Chaiken, J-PAL North America, and archived by Travis Donahoe, Harvard University. Additional information edited by Michael Darisse and Lars Vilhuber, Cornell University and American Economic Association.

This topic is no longer available in the NCHS Data Query System (DQS). Search, visualize, and download other estimates from over 120 health topics with DQS, available from: https://www.cdc.gov/nchs/dataquery/index.htm. Data on on average annual infant mortality rates in the United States and U.S. dependent areas, by race and Hispanic origin of mother, state, and territory. Data are from Health, United States. SOURCE: National Center for Health Statistics, National Vital Statistics System, Linked Birth/Infant Death Data Set.

"ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) created Centers for Disease Control and Prevention Social Vulnerability Index (CDC SVI or simply SVI, hereafter) to help public health officials and emergency response planners identify and map the communities that will most likely need support before, during, and after a hazardous event.

SVI indicates the relative vulnerability of every U.S. Census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. SVI ranks the tracts on 15 social factors, including unemployment, minority status, and disability, and further groups them into four related themes. Thus, each tract receives a ranking for each Census variable and for each of the four themes, as well as an overall ranking."

For more see https://www.atsdr.cdc.gov/place-health/php/svi/svi-data-documentation-download.html

This dataset contains forecasted weekly numbers of reported COVID-19 incident cases, incident deaths, and cumulative deaths in the United States, previously reported on COVID Data Tracker (https://covid.cdc.gov/covid-data-tracker/#datatracker-home). These forecasts were generated using mathematical models by CDC partners in the COVID-19 Forecast Hub (https://covid19forecasthub.org/doc/ensemble/). A CDC ensemble model was produced every week using the submitted models from that week at the national, and state/territory level.

This dataset is intended to mirror the observed and forecasted data, previously available for download on the CDC’s COVID Data Tracker. Mortality forecasts for both new and cumulative reported COVID-19 deaths were produced at the state and territory level and national level. Forecasts of new reported COVID-19 cases were produced at the county, state/territory, and national level. Please note that this dataset is not complete for every model, date, location or combination thereof. Specifically, county level submissions for COVID-19 incident cases were accepted, but not required, and are missing or incomplete for many models and dates. State and territory-level forecasts are more complete, but not all models submitted forecasts for all locations, dates, and targets (new reported deaths, new reported cases, and cumulative reported deaths). Forecasts for COVID-19 incident cases were discontinued in February 2022. Forecasts for COVID-19 cumulative and incident deaths were discontinued in March 2023.

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 is the Social Vulnerability Index (SVI) developed by the U.S. Centers for Disease Control (CDC) [1]. This is often used by the emergency response community to anticipate areas where social support systems are weaker, and residents may be more likely to need help. A map viewer for the national database can be found here [2]. Documentation is available here [3] which is also included for download below.

Subsets of the national coverage for the Hurricane Harvey and Hurricane Irma hydrologic study areas can be downloaded below.

[1] SVI web site [http://svi.cdc.gov] [2] CDC’s Social Vulnerability Index (SVI) – 2014 overall SVI, census tract level (web feature layer) [http://cuahsi.maps.arcgis.com/home/item.html?id=f951e0df78604cf0ab1fda61a575be6b] [3] SVI Documentation [https://svi.cdc.gov/Documents/Data/2014_SVI_Data/SVI2014Documentation.pdf] [4] ArcGIS Online feature service (CONUS) [https://services3.arcgis.com/ZvidGQkLaDJxRSJ2/arcgis/rest/services/Overall_2014_Tracts/FeatureServer]

This city boundary shapefile was extracted from Esri Data and Maps for ArcGIS 2014 - U.S. Populated Place Areas. This shapefile can be joined to 500 Cities city-level Data (GIS Friendly Format) in a geographic information system (GIS) to make city-level maps.

The USACE South Atlantic Coastal Study’s CDC Social Vulnerability Index leverages the CDC’s published Social Vulnerability Index (SVI) across the SACS study area. The SACS study area includes tidally influenced coastal areas to the inland extent of NOAA’s Category 5 Maximum of Maximum storm surge hazard layer, from North Carolina to Mississippi, including Puerto Rico and the US Virgin Islands.The methodology and original SVI was published in 2011 and was most recently updated in 2014. The Social Vulnerability Index (SVI) indicates the relative vulnerability of every U.S. Census tract. The SVI ranks the tracts on 15 social factors, including unemployment, minority status, and disability, and further groups them into four related themes. Thus each tract receives a ranking for each Census variable and for each of the four themes, as well as an overall ranking.This Tier 1 dataset is available for download here:Tier 1 Risk Assessment DownloadThe SVI contains the following criteria: 1. Socioeconomic Status (ST)Below PovertyUnemployedIncomeNo High School Diploma2. Household Composition and Disability (HCD)Aged 65 or OlderAged 17 or YoungerCivilian with a DisabilitySingle-Parent Households3. Minority Status and Language (MSL)MinoritySpeaks English “less than well”4. Housing and Transportation (HT)Multi-Unit StructuresMobile HomesCrowdingNo VehicleGroup QuartersThe SACS CDC Social Vulnerability Index was developed by ranking all census tracts within the study area on the RPL theme value of the CDC’s SVI dataset, and then normalizing this percentile index ranking on a value of 0 to 1. The resulting dataset was then converted to a grid using this normalized value. For more information regarding the CDC’s methodology, please reference the following:https://svi.cdc.gov/https://svi.cdc.gov/Documents/Data/A%20Social%20Vulnerability%20Index%20for%20Disaster%20Management.pdfhttps://svi.cdc.gov/Documents/Data/2016_SVI_Data/SVI2016Documentation.pdf

Reporting of new Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. This dataset will receive a final update on June 1, 2023, to reconcile historical data through May 10, 2023, and will remain publicly available.

Aggregate Data Collection Process Since the start of the COVID-19 pandemic, data have been gathered through a robust process with the following steps:

• A CDC data team reviews and validates the information obtained from jurisdictions’ state and local websites via an overnight data review process.
• If more than one official county data source exists, CDC uses a comprehensive data selection process comparing each official county data source, and takes the highest case and death counts respectively, unless otherwise specified by the state.
• CDC compiles these data and posts the finalized information on COVID Data Tracker.
• County level data is aggregated to obtain state and territory specific totals.

This process is collaborative, with CDC and jurisdictions working together to ensure the accuracy of COVID-19 case and death numbers. County counts provide the most up-to-date numbers on cases and deaths by report date. CDC may retrospectively update counts to correct data quality issues.

Methodology Changes Several differences exist between the current, weekly-updated dataset and the archived version:

• Source: The current Weekly-Updated Version is based on county-level aggregate count data, while the Archived Version is based on State-level aggregate count data.
• Confirmed/Probable Cases/Death breakdown:  While the probable cases and deaths are included in the total case and total death counts in both versions (if applicable), they were reported separately from the confirmed cases and deaths by jurisdiction in the Archived Version.  In the current Weekly-Updated Version, the counts by jurisdiction are not reported by confirmed or probable status (See Confirmed and Probable Counts section for more detail).
• Time Series Frequency: The current Weekly-Updated Version contains weekly time series data (i.e., one record per week per jurisdiction), while the Archived Version contains daily time series data (i.e., one record per day per jurisdiction).
• Update Frequency: The current Weekly-Updated Version is updated weekly, while the Archived Version was updated twice daily up to October 20, 2022.

Important note: The counts reflected during a given time period in this dataset may not match the counts reflected for the same time period in the archived dataset noted above. Discrepancies may exist due to differences between county and state COVID-19 case surveillance and reconciliation efforts.

Confirmed and Probable Counts In this dataset, counts by jurisdiction are not displayed by confirmed or probable status. Instead, confirmed and probable cases and deaths are included in the Total Cases and Total Deaths columns, when available. Not all jurisdictions report probable cases and deaths to CDC.* Confirmed and probable case definition criteria are described here:

Council of State and Territorial Epidemiologists (ymaws.com).

Deaths CDC reports death data on other sections of the website: CDC COVID Data Tracker: Home, CDC COVID Data Tracker: Cases, Deaths, and Testing, and NCHS Provisional Death Counts. Information presented on the COVID Data Tracker pages is based on the same source (total case counts) as the present dataset; however, NCHS Death Counts are based on death certificates that use information reported by physicians, medical examiners, or coroners in the cause-of-death section of each certificate. Data from each of these pages are considered provisional (not complete and pending verification) and are therefore subject to change. Counts from previous weeks are continually revised as more records are received and processed.

Number of Jurisdictions Reporting There are currently 60 public health jurisdictions reporting cases of COVID-19. This includes the 50 states, the District of Columbia, New York City, the U.S. territories of American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, Puerto Rico, and the U.S Virgin Islands as well as three independent countries in compacts of free association with the United States, Federated States of Micronesia, Republic of the Marshall Islands, and Republic of Palau. New York State’s reported case and death counts do not include New York City’s counts as they separately report nationally notifiable conditions to CDC.

CDC COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths, available by state and by county. These and other data on COVID-19 are available from multiple public locations, such as:

https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html

https://www.cdc.gov/covid-data-tracker/index.html

https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html

https://www.cdc.gov/coronavirus/2019-ncov/php/open-america/surveillance-data-analytics.html

Additional COVID-19 public use datasets, include line-level (patient-level) data, are available at: https://data.cdc.gov/browse?tags=covid-19.

Archived Data Notes:

November 3, 2022: Due to a reporting cadence issue, case rates for Missouri counties are calculated based on 11 days’ worth of case count data in the Weekly United States COVID-19 Cases and Deaths by State data released on November 3, 2022, instead of the customary 7 days’ worth of data.

November 10, 2022: Due to a reporting cadence change, case rates for Alabama counties are calculated based on 13 days’ worth of case count data in the Weekly United States COVID-19 Cases and Deaths by State data released on November 10, 2022, instead of the customary 7 days’ worth of data.

November 10, 2022: Per the request of the jurisdiction, cases and deaths among non-residents have been removed from all Hawaii county totals throughout the entire time series. Cumulative case and death counts reported by CDC will no longer match Hawaii’s COVID-19 Dashboard, which still includes non-resident cases and deaths. 

November 17, 2022: Two new columns, weekly historic cases and weekly historic deaths, were added to this dataset on November 17, 2022. These columns reflect case and death counts that were reported that week but were historical in nature and not reflective of the current burden within the jurisdiction. These historical cases and deaths are not included in the new weekly case and new weekly death columns; however, they are reflected in the cumulative totals provided for each jurisdiction. These data are used to account for artificial increases in case and death totals due to batched reporting of historical data.

December 1, 2022: Due to cadence changes over the Thanksgiving holiday, case rates for all Ohio counties are reported as 0 in the data released on December 1, 2022.

January 5, 2023: Due to North Carolina’s holiday reporting cadence, aggregate case and death data will contain 14 days’ worth of data instead of the customary 7 days. As a result, case and death metrics will appear higher than expected in the January 5, 2023, weekly release.

January 12, 2023: Due to data processing delays, Mississippi’s aggregate case and death data will be reported as 0. As a result, case and death metrics will appear lower than expected in the January 12, 2023, weekly release.

January 19, 2023: Due to a reporting cadence issue, Mississippi’s aggregate case and death data will be calculated based on 14 days’ worth of data instead of the customary 7 days in the January 19, 2023, weekly release.

January 26, 2023: Due to a reporting backlog of historic COVID-19 cases, case rates for two Michigan counties (Livingston and Washtenaw) were higher than expected in the January 19, 2023 weekly release.

January 26, 2023: Due to a backlog of historic COVID-19 cases being reported this week, aggregate case and death counts in Charlotte County and Sarasota County, Florida, will appear higher than expected in the January 26, 2023 weekly release.

January 26, 2023: Due to data processing delays, Mississippi’s aggregate case and death data will be reported as 0 in the weekly release posted on January 26, 2023.

February 2, 2023: As of the data collection deadline, CDC observed an abnormally large increase in aggregate COVID-19 cases and deaths reported for Washington State. In response, totals for new cases and new deaths released on February 2, 2023, have been displayed as zero at the state level until the issue is addressed with state officials. CDC is working with state officials to address the issue.

February 2, 2023: Due to a decrease reported in cumulative case counts by Wyoming, case rates will be reported as 0 in the February 2, 2023, weekly release. CDC is working with state officials to verify the data submitted.

February 16, 2023: Due to data processing delays, Utah’s aggregate case and death data will be reported as 0 in the weekly release posted on February 16, 2023. As a result, case and death metrics will appear lower than expected and should be interpreted with caution.

February 16, 2023: Due to a reporting cadence change, Maine’s

This dataset is used to showcase in Jupyter notebooks the usage of the Pythie postprocessing software available on GitHub.

We use the ERA5 reanalysis over a large area in Europe from 1997 to 2016 as gridded observations. These reanalysis have been downloaded from the Copernicus Data Store in GRIB format and converted to the NetCDF file format.

The reforecasts files have been download from ECMWF and converted to NetCDF files.

The observation data of the WMO-compliant DWD meteorological station of Soltau from 1997 to 2016.
The station is located at the point 52°57'37.5"N, 9°47'35.0"E. The data have been downloaded from the DWD Climate Data Center.

Gridded reforecast data source

Source www.ecmwf.int

Creative Commons Attribution 4.0 International (CC BY 4.0)
Copyright © 2021 European Centre for Medium-Range Weather Forecasts (ECMWF).

Copernicus ERA5 gridded reanalysis data source

Source https://cds.climate.copernicus.eu/

Copyright © 2021 European Union.

Generated using Copernicus Climate Change Service information 2021.

Hersbach et al. (2018): ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on < 21-04-2021 >), doi:10.24381/cds.adbb2d47.

Observation data source

Source: Deutscher Wetterdienst, DWD CDC portal

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,

The CDC/ATDSR developed a national Social Vulnerability Index (SVI) to bring together many different factors at once and estimate places in greatest need during an emergency. This was done with a national-level analysis and does not account for the impact of Arizona-specific conditions on a community’s vulnerability such as extreme heat. The Arizona Social Vulnerability Index (AZSVI) incorporates an additional theme (Arizona Theme 5) into the index using factors determined by the Arizona Health Improvement Plan (AzHIP) Data Advisory Committee.The AZSVI presents factors that Arizona communities face as they pursue health, community strength, and data to inform action. The AZSVI provides the Arizona public health workforce, health care providers, policy makers and public a tool to assess the factors impacting Arizona communities, with the aim of addressing disparities and fostering equity. The AZSVI is a product of the Arizona Health Improvement Plan (AzHIP) Data Advisory Committee, created in partnership with Arizona State University, Arizona Department of Health Services GIS, and the ADHS Office of Health Equity. Funding for this project was provided through the Centers for Disease Control and Prevention (CDC) Health Disparities Grant OT21 2103. Data may be downloaded in full or in part, by adding a filter before selecting your download file type. To view information about field definitions, data sources, and analysis methods for the Arizona Theme 5, download this data documentation:Technical Data DocumentationTechnical Data DictionaryTo view data documentation for the first four themes, which come directly from the CDC/ATSDR SVI, please visit their website and select "CDC/ATSDR SVI Documentation 2020".