86 datasets found
  1. Weekly United States COVID-19 Cases and Deaths by State - ARCHIVED

    • data.cdc.gov
    • data.virginia.gov
    • +1more
    application/rdfxml +5
    Updated Jun 1, 2023
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    CDC COVID-19 Response (2023). Weekly United States COVID-19 Cases and Deaths by State - ARCHIVED [Dataset]. https://data.cdc.gov/Case-Surveillance/Weekly-United-States-COVID-19-Cases-and-Deaths-by-/pwn4-m3yp
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    csv, application/rdfxml, xml, tsv, json, application/rssxmlAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    Authors
    CDC COVID-19 Response
    License

    https://www.usa.gov/government-workshttps://www.usa.gov/government-works

    Area covered
    United States
    Description

    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

  2. n

    Coronavirus (Covid-19) Data in the United States

    • nytimes.com
    • openicpsr.org
    • +2more
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    New York Times, Coronavirus (Covid-19) Data in the United States [Dataset]. https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html
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    Dataset provided by
    New York Times
    Description

    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.

  3. Data from: COVID-19 Case Surveillance Public Use Data with Geography

    • data.cdc.gov
    • data.virginia.gov
    • +4more
    application/rdfxml +5
    Updated Jul 9, 2024
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    CDC Data, Analytics and Visualization Task Force (2024). COVID-19 Case Surveillance Public Use Data with Geography [Dataset]. https://data.cdc.gov/Case-Surveillance/COVID-19-Case-Surveillance-Public-Use-Data-with-Ge/n8mc-b4w4
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    application/rssxml, csv, tsv, application/rdfxml, xml, jsonAvailable download formats
    Dataset updated
    Jul 9, 2024
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    Authors
    CDC Data, Analytics and Visualization Task Force
    License

    https://www.usa.gov/government-workshttps://www.usa.gov/government-works

    Description

    Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

    Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

    This case surveillance public use dataset has 19 elements for all COVID-19 cases shared with CDC and includes demographics, geography (county and state of residence), any exposure history, disease severity indicators and outcomes, and presence of any underlying medical conditions and risk behaviors.

    Currently, CDC provides the public with three versions of COVID-19 case surveillance line-listed data: this 19 data element dataset with geography, a 12 data element public use dataset, and a 33 data element restricted access dataset.

    The following apply to the public use datasets and the restricted access dataset:

    Overview

    The COVID-19 case surveillance database includes individual-level data reported to U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020, to clarify the interpretation of antigen detection tests and serologic test results within the case classification (Interim-20-ID-02). The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data are collected by jurisdictions and reported voluntarily to CDC.

    For more information: NNDSS Supports the COVID-19 Response | CDC.

    COVID-19 Case Reports COVID-19 case reports are routinely submitted to CDC by public health jurisdictions using nationally standardized case reporting forms. On April 5, 2020, CSTE released an Interim Position Statement with national surveillance case definitions for COVID-19. Current versions of these case definitions are available at: https://ndc.services.cdc.gov/case-definitions/coronavirus-disease-2019-2021/. All cases reported on or after were requested to be shared by public health departments to CDC using the standardized case definitions for lab-confirmed or probable cases. On May 5, 2020, the standardized case reporting form was revised. States and territories continue to use this form.

    Data are Considered Provisional

    • The COVID-19 case surveillance data are dynamic; case reports can be modified at any time by the jurisdictions sharing COVID-19 data with CDC. CDC may update prior cases shared with CDC based on any updated information from jurisdictions. For instance, as new information is gathered about previously reported cases, health departments provide updated data to CDC. As more information and data become available, analyses might find changes in surveillance data and trends during a previously reported time window. Data may also be shared late with CDC due to the volume of COVID-19 cases.
    • Annual finalized data: To create the final NNDSS data used in the annual tables, CDC works carefully with the reporting jurisdictions to reconcile the data received during the year until each state or territorial epidemiologist confirms that the data from their area are correct.

    Access Addressing Gaps in Public Health Reporting of Race and Ethnicity for COVID-19, a report from the Council of State and Territorial Epidemiologists, to better understand the challenges in completing race and ethnicity data for COVID-19 and recommendations for improvement.

    Data Limitations

    To learn more about the limitations in using case surveillance data, visit FAQ: COVID-19 Data and Surveillance.

    Data Quality Assurance Procedures

    CDC’s Case Surveillance Section routinely performs data quality assurance procedures (i.e., ongoing corrections and logic checks to address data errors). To date, the following data cleaning steps have been implemented:

    • Questions that have been left unanswered (blank) on the case report form are reclassified to a Missing value, if applicable to the question. For example, in the question "Was the individual hospitalized?" where the possible answer choices include "Yes," "No," or "Unknown," the blank value is recoded to "Missing" because the case report form did not include a response to the question.
    • Logic checks are performed for date data. If an illogical date has been provided, CDC reviews the data with the reporting jurisdiction. For example, if a symptom onset date in the future is reported to CDC, this value is set to null until the reporting jurisdiction updates the date appropriately.
    • Additional data quality processing to recode free text data is ongoing. Data on symptoms, race, ethnicity, and healthcare worker status have been prioritized.

    Data Suppression

    To prevent release of data that could be used to identify people, data cells are suppressed for low frequency (<11 COVID-19 case records with a given values). Suppression includes low frequency combinations of case month, geographic characteristics (county and state of residence), and demographic characteristics (sex, age group, race, and ethnicity). Suppressed values are re-coded to the NA answer option; records with data suppression are never removed.

    Additional COVID-19 Data

    COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths by state and by county. These and other COVID-19 data are available from multiple public locations: COVID Data Tracker; United States COVID-19 Cases and Deaths by State; COVID-19 Vaccination Reporting Data Systems; and COVID-19 Death Data and Resources.

    Notes:

    March 1, 2022: The "COVID-19 Case Surveillance Public Use Data with Geography" will be updated on a monthly basis.

    April 7, 2022: An adjustment was made to CDC’s cleaning algorithm for COVID-19 line level case notification data. An assumption in CDC's algorithm led to misclassifying deaths that were not COVID-19 related. The algorithm has since been revised, and this dataset update reflects corrected individual level information about death status for all cases collected to date.

    June 25, 2024: An adjustment

  4. d

    Johns Hopkins COVID-19 Case Tracker

    • data.world
    csv, zip
    Updated Jul 17, 2025
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    The Associated Press (2025). Johns Hopkins COVID-19 Case Tracker [Dataset]. https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker
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    zip, csvAvailable download formats
    Dataset updated
    Jul 17, 2025
    Authors
    The Associated Press
    Time period covered
    Jan 22, 2020 - Mar 9, 2023
    Area covered
    Description

    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.

    • 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

    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

  5. d

    COVID-19 case rate per 100,000 population and percent test positivity in the...

    • datasets.ai
    • data.ct.gov
    • +1more
    23, 40, 55, 8
    Updated Sep 8, 2024
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    State of Connecticut (2024). COVID-19 case rate per 100,000 population and percent test positivity in the last 7 days by town - ARCHIVE [Dataset]. https://datasets.ai/datasets/covid-19-case-rate-per-100000-population-and-percent-test-positivity-in-the-last-7-days-by
    Explore at:
    23, 55, 40, 8Available download formats
    Dataset updated
    Sep 8, 2024
    Dataset authored and provided by
    State of Connecticut
    Description

    DPH note about change from 7-day to 14-day metrics: As of 10/15/2020, this dataset is no longer being updated. Starting on 10/15/2020, these metrics will be calculated using a 14-day average rather than a 7-day average. The new dataset using 14-day averages can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/hree-nys2

    As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

    With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

    This dataset includes a weekly count and weekly rate per 100,000 population for COVID-19 cases, a weekly count of COVID-19 PCR diagnostic tests, and a weekly percent positivity rate for tests among people living in community settings. Dates are based on date of specimen collection (cases and positivity).

    A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

    These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

    These data are updated weekly; the previous week period for each dataset is the previous Sunday-Saturday, known as an MMWR week (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). The date listed is the date the dataset was last updated and corresponds to a reporting period of the previous MMWR week. For instance, the data for 8/20/2020 corresponds to a reporting period of 8/9/2020-8/15/2020.

    Notes: 9/25/2020: Data for Mansfield and Middletown for the week of Sept 13-19 were unavailable at the time of reporting due to delays in lab reporting.

  6. d

    Data from: Prevalence and characteristics of long COVID-19 in Jordan: A...

    • datadryad.org
    • data.niaid.nih.gov
    • +2more
    zip
    Updated Dec 22, 2023
    + more versions
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    Marya Obeidat (2023). Prevalence and characteristics of long COVID-19 in Jordan: A cross sectional survey [Dataset]. http://doi.org/10.5061/dryad.4b8gthtk6
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    zipAvailable download formats
    Dataset updated
    Dec 22, 2023
    Dataset provided by
    Dryad
    Authors
    Marya Obeidat
    Time period covered
    2023
    Description

    Long COVID-19 in Jordan

    https://doi.org/10.5061/dryad.4b8gthtk6

    The data represent responses to a self-reporting questionnaire that was designed to address long COVID-19 status and factors that may associate with it among Jordanians. It included questions regarding COVID-19 symptoms, pre-existing medical history, treatment and supplements, COVID-19 vaccination history, and symptoms recorded after vaccination. We adopted the definition of long COVID-19 that refers to individuals experiencing at least one symptom longer than four weeks.

    Description of the data and file structure

    The data were entered into SPSS data file and organized as follows: Demographic data (columns B-H) are sex (Male:0, Female: 1), age (18-34:2, 35-44:3, 45-54:4, >55: 5), marital status (single:1, married:2, other:3), smoking (No:0, Yes:1), employment status (not:0, goverment:1, private:2), and obesity (non- obese:0, obese:1), hospitalization required (column I, No:0, Yes:1), number of times of infected w...

  7. COVID-19 Time-Series Metrics by County and State (ARCHIVED)

    • data.chhs.ca.gov
    • data.ca.gov
    • +2more
    csv, xlsx, zip
    Updated Aug 28, 2024
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    California Department of Public Health (2024). COVID-19 Time-Series Metrics by County and State (ARCHIVED) [Dataset]. https://data.chhs.ca.gov/dataset/covid-19-time-series-metrics-by-county-and-state
    Explore at:
    csv(7729431), csv(4836928), xlsx(6471), csv(6223281), csv(3313), xlsx(7811), zip, xlsx(11305)Available download formats
    Dataset updated
    Aug 28, 2024
    Dataset authored and provided by
    California Department of Public Healthhttps://www.cdph.ca.gov/
    Description

    Note: This COVID-19 data set is no longer being updated as of December 1, 2023. Access current COVID-19 data on the CDPH respiratory virus dashboard (https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/Respiratory-Viruses/RespiratoryDashboard.aspx) or in open data format (https://data.chhs.ca.gov/dataset/respiratory-virus-dashboard-metrics).

    As of August 17, 2023, data is being updated each Friday.

    For death data after December 31, 2022, California uses Provisional Deaths from the Center for Disease Control and Prevention’s National Center for Health Statistics (NCHS) National Vital Statistics System (NVSS). Prior to January 1, 2023, death data was sourced from the COVID-19 registry. The change in data source occurred in July 2023 and was applied retroactively to all 2023 data to provide a consistent source of death data for the year of 2023.

    As of May 11, 2023, data on cases, deaths, and testing is being updated each Thursday. Metrics by report date have been removed, but previous versions of files with report date metrics are archived below.

    All metrics include people in state and federal prisons, US Immigration and Customs Enforcement facilities, US Marshal detention facilities, and Department of State Hospitals facilities. Members of California's tribal communities are also included.

    The "Total Tests" and "Positive Tests" columns show totals based on the collection date. There is a lag between when a specimen is collected and when it is reported in this dataset. As a result, the most recent dates on the table will temporarily show NONE in the "Total Tests" and "Positive Tests" columns. This should not be interpreted as no tests being conducted on these dates. Instead, these values will be updated with the number of tests conducted as data is received.

  8. United States COVID-19 Community Levels by County

    • data.cdc.gov
    • data.virginia.gov
    • +1more
    application/rdfxml +5
    Updated Nov 2, 2023
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    CDC COVID-19 Response (2023). United States COVID-19 Community Levels by County [Dataset]. https://data.cdc.gov/Public-Health-Surveillance/United-States-COVID-19-Community-Levels-by-County/3nnm-4jni
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    application/rdfxml, application/rssxml, csv, tsv, xml, jsonAvailable download formats
    Dataset updated
    Nov 2, 2023
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    Authors
    CDC COVID-19 Response
    License

    https://www.usa.gov/government-workshttps://www.usa.gov/government-works

    Area covered
    United States
    Description

    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,

  9. D

    ARCHIVED: COVID-19 Cases by Population Characteristics Over Time

    • data.sfgov.org
    • healthdata.gov
    • +2more
    application/rdfxml +5
    Updated Sep 11, 2023
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    (2023). ARCHIVED: COVID-19 Cases by Population Characteristics Over Time [Dataset]. https://data.sfgov.org/Health-and-Social-Services/ARCHIVED-COVID-19-Cases-by-Population-Characterist/j7i3-u9ke
    Explore at:
    xml, csv, json, application/rdfxml, tsv, application/rssxmlAvailable download formats
    Dataset updated
    Sep 11, 2023
    License

    ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
    License information was derived automatically

    Description

    A. SUMMARY This archived dataset includes data for population characteristics that are no longer being reported publicly. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.

    B. HOW THE DATASET IS CREATED Data on the population characteristics of COVID-19 cases are from:  * Case interviews  * Laboratories  * Medical providers    These multiple streams of data are merged, deduplicated, and undergo data verification processes.  

    Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases. * The population estimates for the "Other" or “Multi-racial” groups should be considered with caution. The Census definition is likely not exactly aligned with how the City collects this data. For that reason, we do not recommend calculating population rates for these groups.

    Gender * The City collects information on gender identity using these guidelines.

    Skilled Nursing Facility (SNF) occupancy * A Skilled Nursing Facility (SNF) is a type of long-term care facility that provides care to individuals, generally in their 60s and older, who need functional assistance in their daily lives.  * This dataset includes data for COVID-19 cases reported in Skilled Nursing Facilities (SNFs) through 12/31/2022, archived on 1/5/2023. These data were identified where “Characteristic_Type” = ‘Skilled Nursing Facility Occupancy’.

    Sexual orientation * The City began asking adults 18 years old or older for their sexual orientation identification during case interviews as of April 28, 2020. Sexual orientation data prior to this date is unavailable. * The City doesn’t collect or report information about sexual orientation for persons under 12 years of age. * Case investigation interviews transitioned to the California Department of Public Health, Virtual Assistant information gathering beginning December 2021. The Virtual Assistant is only sent to adults who are 18+ years old. https://www.sfdph.org/dph/files/PoliciesProcedures/COM9_SexualOrientationGuidelines.pdf">Learn more about our data collection guidelines pertaining to sexual orientation.

    Comorbidities * Underlying conditions are reported when a person has one or more underlying health conditions at the time of diagnosis or death.

    Homelessness Persons are identified as homeless based on several data sources: * self-reported living situation * the location at the time of testing * Department of Public Health homelessness and health databases * Residents in Single-Room Occupancy hotels are not included in these figures. These methods serve as an estimate of persons experiencing homelessness. They may not meet other homelessness definitions.

    Single Room Occupancy (SRO) tenancy * SRO buildings are defined by the San Francisco Housing Code as having six or more "residential guest rooms" which may be attached to shared bathrooms, kitchens, and living spaces. * The details of a person's living arrangements are verified during case interviews.

    Transmission Type * Information on transmission of COVID-19 is based on case interviews with individuals who have a confirmed positive test. Individuals are asked if they have been in close contact with a known COVID-19 case. If they answer yes, transmission category is recorded as contact with a known case. If they report no contact with a known case, transmission category is recorded as community transmission. If the case is not interviewed or was not asked the question, they are counted as unknown.

    C. UPDATE PROCESS This dataset has been archived and will no longer update as of 9/11/2023.

    D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

    This dataset includes many different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of cases on each date.

    New cases are the count of cases within that characteristic group where the positive tests were collected on that specific specimen collection date. Cumulative cases are the running total of all San Francisco cases in that characteristic group up to the specimen collection date listed.

    This data may not be immediately available for recently reported cases. Data updates as more information becomes available.

    To explore data on the total number of cases, use the ARCHIVED: COVID-19 Cases Over Time dataset.

    E. CHANGE LOG

    • 9/11/2023 - data on COVID-19 cases by population characteristics over time are no longer being updated. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.
    • 6/6/2023 - data on cases by transmission type have been removed. See section ARCHIVED DATA for more detail.
    • 5/16/2023 - data on cases by sexual orientation, comorbidities, homelessness, and single room occupancy have been removed. See section ARCHIVED DATA for more detail.
    • 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
    • 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
    • 1/31/2023 - updated “population_estimate” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
    • 1/5/2023 - data on SNF cases removed. See section ARCHIVED DATA for more detail.
    • 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
    • 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
    • 7/15/2022 - reinfections added to cases dataset. See section SUMMARY for more information on how reinfections are identified.

  10. O

    COVID-19 case rate per 100,000 population and percent test positivity in the...

    • data.ct.gov
    • catalog.data.gov
    application/rdfxml +5
    Updated Jun 23, 2022
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    Department of Public Health (2022). COVID-19 case rate per 100,000 population and percent test positivity in the last 14 days by town - ARCHIVE [Dataset]. https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/hree-nys2
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    application/rssxml, xml, csv, json, tsv, application/rdfxmlAvailable download formats
    Dataset updated
    Jun 23, 2022
    Dataset authored and provided by
    Department of Public Health
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    Note: 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.

    This dataset includes a count and rate per 100,000 population for COVID-19 cases, a count of COVID-19 molecular diagnostic tests, and a percent positivity rate for tests among people living in community settings for the previous two-week period. Dates are based on date of specimen collection (cases and positivity).

    A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

    Percent positivity is calculated as the number of positive tests among community residents conducted during the 14 days divided by the total number of positive and negative tests among community residents during the same period. If someone was tested more than once during that 14 day period, then those multiple test results (regardless of whether they were positive or negative) are included in the calculation.

    These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

    These data are updated weekly and reflect the previous two full Sunday-Saturday (MMWR) weeks (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf).

    DPH note about change from 7-day to 14-day metrics: Prior to 10/15/2020, these metrics were calculated using a 7-day average rather than a 14-day average. The 7-day metrics are no longer being updated as of 10/15/2020 but the archived dataset can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/s22x-83rd

    As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

    With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

    Additional notes: As of 11/5/2020, CT DPH has added antigen testing for SARS-CoV-2 to reported test counts in this dataset. The tests included in this dataset include both molecular and antigen datasets. Molecular tests reported include polymerase chain reaction (PCR) and nucleic acid amplicfication (NAAT) tests.

    The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

    Data suppression is applied when the rate is <5 cases per 100,000 or if there are <5 cases within the town. Information on why data suppression rules are applied can be found online here: https://www.cdc.gov/cancer/uscs/technical_notes/stat_methods/suppression.htm

  11. A

    ‘COVID-19 State Data’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Mar 31, 2020
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2020). ‘COVID-19 State Data’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-covid-19-state-data-85fa/4a8c7dec/?iid=002-627&v=presentation
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    Dataset updated
    Mar 31, 2020
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘COVID-19 State Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/nightranger77/covid19-state-data on 28 January 2022.

    --- Dataset description provided by original source is as follows ---

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

    Deaths, Infections and Tests by State

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

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

    Predictor Data and Sources

    Population (2020)

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

    ICU Beds and Age 60+

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

    GDP

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

    Income per capita (2018)

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

    Gini

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

    Unemployment (2020)

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

    Sex (2017)

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

    Smoking Percentage (2020)

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

    Influenza and Pneumonia Death Rate (2018)

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

    Chronic Lower Respiratory Disease Death Rate (2018)

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

    Active Physicians (2019)

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

    Hospitals (2018)

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

    Health spending per capita

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

    Pollution (2019)

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

    Medium and Large Airports

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

    Temperature (2019)

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

    Urbanization (2010)

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

    Age Groups (2018)

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

    School Closure Dates

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

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

    --- Original source retains full ownership of the source dataset ---

  12. D

    ARCHIVED: COVID-19 Cases and Deaths Summarized by Geography

    • data.sfgov.org
    Updated Sep 11, 2023
    + more versions
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    Department of Public Health - Population Health Division (2023). ARCHIVED: COVID-19 Cases and Deaths Summarized by Geography [Dataset]. https://data.sfgov.org/COVID-19/ARCHIVED-COVID-19-Cases-and-Deaths-Summarized-by-G/tpyr-dvnc
    Explore at:
    xml, application/rdfxml, csv, tsv, application/geo+json, kml, application/rssxml, kmzAvailable download formats
    Dataset updated
    Sep 11, 2023
    Dataset authored and provided by
    Department of Public Health - Population Health Division
    License

    ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
    License information was derived automatically

    Description

    A. SUMMARY Medical provider confirmed COVID-19 cases and confirmed COVID-19 related deaths in San Francisco, CA aggregated by several different geographic areas and normalized by 2016-2020 American Community Survey (ACS) 5-year estimates for population data to calculate rate per 10,000 residents.

    On September 12, 2021, a new case definition of COVID-19 was introduced that includes criteria for enumerating new infections after previous probable or confirmed infections (also known as reinfections). A reinfection is defined as a confirmed positive PCR lab test more than 90 days after a positive PCR or antigen test. The first reinfection case was identified on December 7, 2021.

    Cases and deaths are both mapped to the residence of the individual, not to where they were infected or died. For example, if one was infected in San Francisco at work but lives in the East Bay, those are not counted as SF Cases or if one dies in Zuckerberg San Francisco General but is from another county, that is also not counted in this dataset.

    Dataset is cumulative and covers cases going back to 3/2/2020 when testing began.

    Geographic areas summarized are: 1. Analysis Neighborhoods 2. Census Tracts 3. Census Zip Code Tabulation Areas

    B. HOW THE DATASET IS CREATED Addresses from medical data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area. The 2016-2020 American Community Survey (ACS) population estimates provided by the Census are used to create a rate which is equal to ([count] / [acs_population]) * 10000) representing the number of cases per 10,000 residents.

    C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset daily at 7:30 Pacific Time.

    D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

    Privacy rules in effect To protect privacy, certain rules are in effect: 1. Case counts greater than 0 and less than 10 are dropped - these will be null (blank) values 2. Death counts greater than 0 and less than 10 are dropped - these will be null (blank) values 3. Cases and deaths dropped altogether for areas where acs_population < 1000

    Rate suppression in effect where counts lower than 20 Rates are not calculated unless the case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology.

    A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are areal representations of routes. Read how the Census develops ZCTAs on their website.

    Row included for Citywide case counts, incidence rate, and deaths A single row is included that has the Citywide case counts and incidence rate. This can be used for comparisons. Citywide will capture all cases regardless of address quality. While some cases cannot be mapped to sub-areas like Census Tracts, ongoing data quality efforts result in improved mapping on a rolling basis.

    E. CHANGE LOG

    • 9/11/2023 - data on COVID-19 cases and deaths summarized by geography are no longer being updated. This data is currently through 9/6/2023 and will not include any new data after this date.
    • 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
    • 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
    • 1/31/2023 - updated “acs_population” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
    • 1/31/2023 - implemented system updates to streamline and improve our geo-coded data, resulting in small shifts in our case and death data by geography.
    • 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
    • 2/23/2022 - the New Cases Map dashboard began pulling from this dataset. To access Cases by Geography Over Time, please refer to this dataset.
    • 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
    • 7/15/2022 - reinfections added to cases dataset. See section SUMMARY for more information on how reinfections are identified.
    • 4/16/2021 - dataset updated to refresh with a five-day data lag.

  13. d

    DOHMH COVID-19 Antibody-by-Week

    • catalog.data.gov
    • data.cityofnewyork.us
    Updated Jul 7, 2024
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    data.cityofnewyork.us (2024). DOHMH COVID-19 Antibody-by-Week [Dataset]. https://catalog.data.gov/dataset/dohmh-covid-19-antibody-by-week
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    Dataset updated
    Jul 7, 2024
    Dataset provided by
    data.cityofnewyork.us
    Description

    This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by week of testing. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/trends/antibody-by-week.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level. These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents. In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders.) Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning. Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020. Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certainly be miscalculating or counting the same values multiple times. To analyze the most current data, only use the latest extract date. Antibody tests that are missing dates are not included in the dataset; as dates are identified, these events are added. Lags between occurrence and report of cases and tests can be assessed by comparing counts and rates across multiple data extract dates. For further details, visit: • https://www1.nyc.gov/site/doh/covid/covid-19-data.pagehttps://github.com/nychealth/coronavirus-data

  14. FOI-01538 - Datasets - Open Data Portal

    • opendata.nhsbsa.net
    Updated Dec 5, 2023
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    nhsbsa.net (2023). FOI-01538 - Datasets - Open Data Portal [Dataset]. https://opendata.nhsbsa.net/dataset/foi-01538
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    Dataset updated
    Dec 5, 2023
    Dataset provided by
    NHS Business Services Authority
    Description

    Request I believe the above scheme needs to be put in place urgently. Can you please answer the following questions: 1. How many people have applied to you for Ill Health Retirement with Long Covid? 2. How many people have been rejected for Tier One and/or Tier Two levels of IHR when applying with Long Covid? 3. What evidence (listing guidance and research evidence) are being used to reject or confirm applications for IHR with Long Covid? Response Question 1 & 2 A copy of the information is attached. Question 3 Each Scheme Medical Adviser (SMA) is expected to adopt evidence-based practice in arriving at a decision. They do this by combining the following: Medical evidence provided in the Scheme member’s application, Further medical evidence that the SMA may have requested from the Scheme member’s treating healthcare professionals, Information that the employer may have provided in Part A of Form AW33E (e.g. demands of the work duties, any workplace adjustments tried, and the effectiveness of such adjustments), Information that the Scheme member may have provided in Part B of Form AW33E (for example, how long COVID affects them), Current medical literature on long COVID, And the SMA’s occupational health expertise. When assessing ill-health retirement applications from scheme members who have long COVID, the SMA might consult the following guidance and research evidence: • The Society of Occupational Medicine (SOM): ‘Long COVID and Return to Work – What Works?’ (https://www.som.org.uk/sites/som.org.uk/files/Long_COVID_and_Return_to_Work_What_Works_0.pdf) • The Faculty of Occupational Medicine (FOM): ‘Guidance for healthcare professionals on return to work for patients with post-COVID syndrome’ (https://www.fom.ac.uk/wp-content/uploads/FOM-Guidance-post-COVID_healthcare-professionals.pdf) • Occupational and Environmental Medicine (academic journal of the FOM: https://oem.bmj.com) • Occupational Medicine (academic journal of the SOM: https://academic.oup.com/occmed?login=false) • Industrial Injuries Advisory Council publication: ‘COVID-19 and Occupational Impacts’ (https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1119955/covid-19-and-occupational-impacts.pdf) • NICE: https://cks.nice.org.uk/topics/long-term-effects-of-coronavirus-long-covid • Nature. An example of a recent publication in this journal is Davis, H., McCorkell, L., Vogel, J. M., & Topol, E. J. (2023). Long covid: major findings, mechanisms and recommendations. Nature Reviews Microbiology, 21(3), 133-146. Full text available at https://www.nature.com/articles/s41579-022-00846-2 • British Medical Journal (BMJ) • Journal of the American Medical Association (JAMA) • The Lancet • New England Journal of Medicine In summary, the SMA is expected to adopt an individual approach to each case and use careful clinical judgement when applying the medical research literature and guidance to the specific medical circumstances of a Scheme member with long COVID. Data Queries If you have any queries regarding the data provided, or if you plan on publishing the data please contact foirequests@nhsbsa.nhs.uk ensuring you quote the above reference. This is important to ensure that the figures are not misunderstood or misrepresented. If you plan on producing a press or broadcast story based upon the data please contact communicationsteam@nhsbsa.nhs.uk This is important to ensure that the figures are not misunderstood or misrepresented.

  15. d

    Long Term Care Dashboard COVID-19 Impacts

    • catalog.data.gov
    • data.kingcounty.gov
    Updated Feb 2, 2024
    + more versions
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    data.kingcounty.gov (2024). Long Term Care Dashboard COVID-19 Impacts [Dataset]. https://catalog.data.gov/dataset/long-term-care-dashboard-covid-19-impacts
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    Dataset updated
    Feb 2, 2024
    Dataset provided by
    data.kingcounty.gov
    Description

    Updated weekly on Thursdays Older adults and people with disabilities who live in long term care facilities are at high risk for COVID-19 illness and death. The data below describes the impacts of COVID-19 on the residents and staff of Long Term Care Facilities licensed by the State Department of Social and Health Services (DSHS), including Skilled Nursing Facilities (nursing homes); Adult Family Homes and Assisted Living Facilities. Cases and deaths are also occurring in other forms of senior housing not licensed by DSHS, including subsidized housing for people age 50+, Permanent Supportive Housing, and naturally occurring retirement communities (NORCs) and among people with disabilities living in Supportive Living Facilities (also licensed by DSHS).

  16. d

    COVID-19 Cases and Deaths by Race/Ethnicity - ARCHIVE

    • catalog.data.gov
    • data.ct.gov
    • +1more
    Updated Aug 12, 2023
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    data.ct.gov (2023). COVID-19 Cases and Deaths by Race/Ethnicity - ARCHIVE [Dataset]. https://catalog.data.gov/dataset/covid-19-cases-and-deaths-by-race-ethnicity
    Explore at:
    Dataset updated
    Aug 12, 2023
    Dataset provided by
    data.ct.gov
    Description

    Note: 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 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update. The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates. The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used. Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical

  17. A

    ‘COVID-19 case rate per 100,000 population and percent test positivity in...

    • analyst-2.ai
    Updated Feb 13, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘COVID-19 case rate per 100,000 population and percent test positivity in the last 14 days by town’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-gov-covid-19-case-rate-per-100000-population-and-percent-test-positivity-in-the-last-14-days-by-town-d334/760f38b9/?iid=006-223&v=presentation
    Explore at:
    Dataset updated
    Feb 13, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘COVID-19 case rate per 100,000 population and percent test positivity in the last 14 days by town’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/d5e87e00-5f12-4c5e-9fb7-9718e5dbef35 on 13 February 2022.

    --- Dataset description provided by original source is as follows ---

    This dataset includes a count and rate per 100,000 population for COVID-19 cases, a count of COVID-19 molecular diagnostic tests, and a percent positivity rate for tests among people living in community settings for the previous two-week period. Dates are based on date of specimen collection (cases and positivity).

    A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

    Percent positivity is calculated as the number of positive tests among community residents conducted during the 14 days divided by the total number of positive and negative tests among community residents during the same period. If someone was tested more than once during that 14 day period, then those multiple test results (regardless of whether they were positive or negative) are included in the calculation.

    These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

    These data are updated weekly and reflect the previous two full Sunday-Saturday (MMWR) weeks (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf).

    DPH note about change from 7-day to 14-day metrics: Prior to 10/15/2020, these metrics were calculated using a 7-day average rather than a 14-day average. The 7-day metrics are no longer being updated as of 10/15/2020 but the archived dataset can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/s22x-83rd

    As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

    With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

    Additional notes: As of 11/5/2020, CT DPH has added antigen testing for SARS-CoV-2 to reported test counts in this dataset. The tests included in this dataset include both molecular and antigen datasets. Molecular tests reported include polymerase chain reaction (PCR) and nucleic acid amplicfication (NAAT) tests.

    The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

    Data suppression is applied when the rate is <5 cases per 100,000 or if there are <5 cases within the town. Information on why data suppression rules are applied can be found online here: https://www.cdc.gov/cancer/uscs/technical_notes/stat_methods/suppression.htm

    --- Original source retains full ownership of the source dataset ---

  18. D

    [Archived] COVID-19 Deaths by Population Characteristics Over Time

    • data.sfgov.org
    • healthdata.gov
    • +1more
    application/rdfxml +5
    Updated Jun 27, 2024
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    (2024). [Archived] COVID-19 Deaths by Population Characteristics Over Time [Dataset]. https://data.sfgov.org/Health-and-Social-Services/-Archived-COVID-19-Deaths-by-Population-Characteri/kkr3-wq7h
    Explore at:
    csv, application/rdfxml, json, tsv, application/rssxml, xmlAvailable download formats
    Dataset updated
    Jun 27, 2024
    License

    ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
    License information was derived automatically

    Description

    As of July 2nd, 2024 the COVID-19 Deaths by Population Characteristics Over Time dataset has been retired. This dataset is archived and will no longer update. We will be publishing a cumulative deaths by population characteristics dataset that will update moving forward.

    A. SUMMARY This dataset shows San Francisco COVID-19 deaths by population characteristics and by date. This data may not be immediately available for recently reported deaths. Data updates as more information becomes available. Because of this, death totals for previous days may increase or decrease. More recent data is less reliable.

    Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how deaths have been distributed among different subgroups. This information can reveal trends and disparities among groups.

    B. HOW THE DATASET IS CREATED As of January 1, 2023, COVID-19 deaths are defined as persons who had COVID-19 listed as a cause of death or a significant condition contributing to their death on their death certificate. This definition is in alignment with the California Department of Public Health and the national https://preparedness.cste.org/wp-content/uploads/2022/12/CSTE-Revised-Classification-of-COVID-19-associated-Deaths.Final_.11.22.22.pdf">Council of State and Territorial Epidemiologists. Death certificates are maintained by the California Department of Public Health.

    Data on the population characteristics of COVID-19 deaths are from: *Case reports *Medical records *Electronic lab reports *Death certificates

    Data are continually updated to maximize completeness of information and reporting on San Francisco COVID-19 deaths.

    To protect resident privacy, we summarize COVID-19 data by only one characteristic at a time. Data are not shown until cumulative citywide deaths reach five or more.

    Data notes on each population characteristic type is listed below.

    Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases.

    Gender * The City collects information on gender identity using these guidelines.

    C. UPDATE PROCESS Updates automatically at 06:30 and 07:30 AM Pacific Time on Wednesday each week.

    Dataset will not update on the business day following any federal holiday.

    D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

    This dataset includes many different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of deaths on each date.

    New deaths are the count of deaths within that characteristic group on that specific date. Cumulative deaths are the running total of all San Francisco COVID-19 deaths in that characteristic group up to the date listed.

    This data may not be immediately available for more recent deaths. Data updates as more information becomes available.

    To explore data on the total number of deaths, use the COVID-19 Deaths Over Time dataset.

    E. CHANGE LOG

    • 9/11/2023 - on this date, we began using an updated definition of a COVID-19 death to align with the California Department of Public Health. This change was applied to COVID-19 deaths retrospectively beginning on 1/1/2023. More information about the recommendation by the Council of State and Territorial Epidemiologists that motivated this change can be found https://preparedness.cste.org/wp-content/uploads/2022/12/CSTE-Revised-Classification-of-COVID-19-associated-Deaths.Final_.11.22.22.pdf">here.
    • 6/6/2023 - data on deaths by transmission type have been removed. See section ARCHIVED DATA for more detail.
    • 5/16/2023 - data on deaths by sexual orientation, comorbidities, homelessness, and single room occupancy have been removed. See section ARCHIVED DATA for more detail.
    • 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
    • 1/31/2023 - column “population_estimate” added.
    • 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
    • 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.

  19. US Adult COVID-19 Impact Survey Data

    • kaggle.com
    Updated Jan 10, 2023
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    The Devastator (2023). US Adult COVID-19 Impact Survey Data [Dataset]. https://www.kaggle.com/datasets/thedevastator/us-adult-covid-19-impact-survey-data
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 10, 2023
    Dataset provided by
    Kaggle
    Authors
    The Devastator
    Area covered
    United States
    Description

    US Adult COVID-19 Impact Survey Data

    Regional, Socio-Economic, and Health Effects

    By Meghan Hoyer [source]

    About this dataset

    The Associated Press is proud to present the COVID Impact Survey, a statistical survey providing data on how the coronavirus pandemic has affected people in the United States. Conducted by NORC at the University of Chicago with sponsorship from the Data Foundation and Federal Reserve Bank of Minneapolis, this probability-based survey offers valuable insight into three core areas related to physical health, economic and financial security, and social and mental health.

    Through this vital survey data, we can gain a better understanding of how individuals are dealing with symptoms related to COVID-19, their financial situation during this time period as well as changes in employment or government assistance policies, food security ization (in both nationwide & regional scope), communication with friends and family members, anxiety levels & if people are volunteering more during pandemic restrictions; furthermore gaining an overall comprehensive snapshot into what factors are impacting public perception regarding COVID-19’s effect on US citizens.

    Using these insights it's possible to track metrics over time - Observing which issues Americans face everyday but also long-term effects such as mental distress or self sacrificing volunteer activities that appear due to underlying stress factors. It’s imperative that we properly weight our analysis when using this data & never report raw numbers; instead we must apply queries using statistical software such R/SPSS - thus being able to find results nationally as well as within 10 states + metropolitan areas across America whilst utilising margin of error for detecting statistically significant differences between each researched segment!

    Let’s open our minds today – digging beneath surface level information so data tells us stories about humanity & our social behavior patterns during these uncertain times!

    More Datasets

    For more datasets, click here.

    Featured Notebooks

    • 🚨 Your notebook can be here! 🚨!

    How to use the dataset

    This dataset contains survey data related to the impact of COVID-19 on US adult residents. The survey covers physical health, mental health, economic security, and social dynamics that have been affected by the pandemic. It is important to remember that this is survey data and must be properly weighted when analyzing it. Raw or aggregated numbers should not be used to generate insights. In order to weight the data appropriately, we recommend using statistical software such as R or SPSS or our provided queries (linked in this guide).

    To generate a table relating to a specific topic covered in the survey, use the survey questionnaire and code book to match a question (the variable label) with its corresponding variable name. For instance “How often have you felt lonely in the past 7 days?” is variable “soc5c”. After entering a variable name into one of our provided queries, a sentence summarizing national results can be written out such as “People in some states are less likely to report loneliness than others… nationally 60% of people said they hadn't felt lonely”

    When making comparisons for numerical statistics between different regions it is important to consider the margin of error associated with each set of surveys for national and regional figures provided within this document; it will help determine if differences between groups are statistically significant. If differences are: at least twice as large as margin of error then there is clear difference; at least as large as margin then there is slight/apparent difference; less than/equal margin no real difference can be determined

    Survey results are generally posted under embargo on Tuesday evenings with data release taking place at 1 pm ET Thursdays afterward under an appropriate title including month & year ie 01_April_30_covid_impact_survey). Data will come in comma-delimited & statistical formats containing necessary inferences regarding sample collection etc outlined within this guide

    When citing survey results these should always attributed with qualification— The Covid Impact Survey conducted by NORC at University Chicago for The Data Foundation sponsored by Federal Reserve Bank Minneapolis & Packard Foundation .
    Lastly more resources regarding AP’s data journalism& distributions capabilities can found via link here or contact kromanoap.org

    Research Ideas

    • Comparing mental health outcomes of the pandemic in different states and metropolitan areas, such as rates of anxiety or lonelines...
  20. COVID-19 US County JHU Data & Demographics

    • kaggle.com
    Updated Mar 1, 2023
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    Heads or Tails (2023). COVID-19 US County JHU Data & Demographics [Dataset]. https://www.kaggle.com/headsortails/covid19-us-county-jhu-data-demographics/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 1, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Heads or Tails
    Area covered
    United States
    Description

    Context

    The United States have recently become the country with the most reported cases of 2019 Novel Coronavirus (COVID-19). This dataset contains daily updated number of reported cases & deaths in the US on the state and county level, as provided by the Johns Hopkins University. In addition, I provide matching demographic information for US counties.

    Content

    The dataset consists of two main csv files: covid_us_county.csv and us_county.csv. See the column descriptions below for more detailed information. In addition, I've added US county shape files for geospatial plots: us_county.shp/dbf/prj/shx.

    • covid_us_county.csv: COVID-19 cases and deaths which will be updated daily. The data is provided by the Johns Hopkins University through their excellent github repo. I combined the separate "confirmed cases" and "deaths" files into a single table, removed a few (I think to be) redundant geo identifier columns, and reshaped the data into long format with a single date column. The earliest recorded cases are from 2020-01-22.

    • us_counties.csv: Demographic information on the US county level based on the (most recent) 2014-18 release of the Amercian Community Survey. Derived via the great tidycensus package.

    Column Description

    COVID-19 dataset covid_us_county.csv:

    • fips: County code in numeric format (i.e. no leading zeros). A small number of cases have NA values here, but can still be used for state-wise aggregation. Currently, this only affect the states of Massachusetts and Missouri.

    • county: Name of the US county. This is NA for the (aggregated counts of the) territories of American Samoa, Guam, Northern Mariana Islands, Puerto Rico, and Virgin Islands.

    • state: Name of US state or territory.

    • state_code: Two letter abbreviation of US state (e.g. "CA" for "California"). This feature has NA values for the territories listed above.

    • lat and long: coordinates of the county or territory.

    • date: Reporting date.

    • cases & deaths: Cumulative numbers for cases & deaths.

    Demographic dataset us_counties.csv:

    • fips, county, state, state_code: same as above. The county names are slightly different, but mostly the difference is that this dataset has the word "County" added. I recommend to join on fips.

    • male & female: Population numbers for male and female.

    • population: Total population for the county. Provided as convenience feature; is always the sum of male + female.

    • female_percentage: Another convenience feature: female / population in percent.

    • median_age: Overall median age for the county.

    Acknowledgements

    Data provided for educational and academic research purposes by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE).

    Licence

    The github repo states that:

    This GitHub repo and its contents herein, including all data, mapping, and analysis, copyright 2020 Johns Hopkins University, all rights reserved, is provided to the public strictly for educational and academic research purposes. The Website relies upon publicly available data from multiple sources, that do not always agree. The Johns Hopkins University hereby disclaims any and all representations and warranties with respect to the Website, including accuracy, fitness for use, and merchantability. Reliance on the Website for medical guidance or use of the Website in commerce is strictly prohibited.
    

    Version history

    • In version 1, a small number of cases had values of `county == "Unassigned". Those have been superseded.
    • Version 5: added US county shape files
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CDC COVID-19 Response (2023). Weekly United States COVID-19 Cases and Deaths by State - ARCHIVED [Dataset]. https://data.cdc.gov/Case-Surveillance/Weekly-United-States-COVID-19-Cases-and-Deaths-by-/pwn4-m3yp
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Weekly United States COVID-19 Cases and Deaths by State - ARCHIVED

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28 scholarly articles cite this dataset (View in Google Scholar)
csv, application/rdfxml, xml, tsv, json, application/rssxmlAvailable download formats
Dataset updated
Jun 1, 2023
Dataset provided by
Centers for Disease Control and Preventionhttp://www.cdc.gov/
Authors
CDC COVID-19 Response
License

https://www.usa.gov/government-workshttps://www.usa.gov/government-works

Area covered
United States
Description

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

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