100+ datasets found
  1. HCUP Visualization of Inpatient Trends in COVID-19 and Other Conditions

    • catalog.data.gov
    • healthdata.gov
    • +2more
    Updated Jul 26, 2023
    + more versions
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    Agency for Healthcare Research and Quality, Department of Health & Human Services (2023). HCUP Visualization of Inpatient Trends in COVID-19 and Other Conditions [Dataset]. https://catalog.data.gov/dataset/hcup-visualization-of-inpatient-trends-in-covid-19-and-other-conditions
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    Dataset updated
    Jul 26, 2023
    Description

    The HCUP Visualization of Inpatient Trends in COVID-19 and Other Conditions displays State-specific monthly trends in inpatient stays related to COVID-19 and other conditions, and facilitates comparisons of the number of hospital discharges, the average length of stays, and in-hospital mortality rates across patient/stay characteristics and States. This information is based on the HCUP State Inpatient Databases (SID), starting with 2018 data, plus newer annual and quarterly inpatient data, if and when available.

  2. covid visualization with leaflet heatmap - Dataset - HiDALGO CKAN portal

    • ckan.hidalgo-project.eu
    Updated Apr 27, 2020
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    ckan.hidalgo-project.eu (2020). covid visualization with leaflet heatmap - Dataset - HiDALGO CKAN portal [Dataset]. https://ckan.hidalgo-project.eu/dataset/jquery-csv-min-js
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    Dataset updated
    Apr 27, 2020
    Dataset provided by
    CKANhttps://ckan.org/
    Description

    Includes a staic and dynamic visualization of sample data of Covid Infections in London. Open: - static visualization of covid infections - dynamicCovidVisualization

  3. d

    Python Code for Visualizing COVID-19 data

    • search.dataone.org
    • borealisdata.ca
    Updated Dec 28, 2023
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    Ryan Chartier; Geoffrey Rockwell (2023). Python Code for Visualizing COVID-19 data [Dataset]. http://doi.org/10.5683/SP3/PYEQL0
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    Dataset updated
    Dec 28, 2023
    Dataset provided by
    Borealis
    Authors
    Ryan Chartier; Geoffrey Rockwell
    Description

    The purpose of this code is to produce a line graph visualization of COVID-19 data. This Jupyter notebook was built and run on Google Colab. This code will serve mostly as a guide and will need to be adapted where necessary to be run locally. The separate COVID-19 datasets uploaded to this Dataverse can be used with this code. This upload is made up of the IPYNB and PDF files of the code.

  4. HCUP Visualization of Inpatient Trends in COVID-19 and Other Conditions -...

    • healthdata.gov
    application/rdfxml +5
    Updated Jul 26, 2023
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    (2023). HCUP Visualization of Inpatient Trends in COVID-19 and Other Conditions - k2dr-3fsc - Archive Repository [Dataset]. https://healthdata.gov/dataset/HCUP-Visualization-of-Inpatient-Trends-in-COVID-19/hy6f-vipk
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    json, xml, application/rssxml, tsv, csv, application/rdfxmlAvailable download formats
    Dataset updated
    Jul 26, 2023
    Description

    This dataset tracks the updates made on the dataset "HCUP Visualization of Inpatient Trends in COVID-19 and Other Conditions" as a repository for previous versions of the data and metadata.

  5. Analytics and Data Visualization for COVID-19 Intelligence

    • coronavirus-disasterresponse.hub.arcgis.com
    Updated Apr 10, 2020
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    Esri’s Disaster Response Program (2020). Analytics and Data Visualization for COVID-19 Intelligence [Dataset]. https://coronavirus-disasterresponse.hub.arcgis.com/documents/disasterresponse::analytics-and-data-visualization-for-covid-19-intelligence/about
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    Dataset updated
    Apr 10, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri’s Disaster Response Program
    Description

    Analytics and Data Visualization for COVID-19 Intelligence.An ArcGIS Blog arcticle that explains how to leverage ready-to-use reports and tutorials to gauge COVID-19 pandemic's impact worldwide._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

  6. G

    Interactive data visualizations of COVID-19 around the world

    • ouvert.canada.ca
    • open.canada.ca
    csv, html
    Updated Sep 24, 2021
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    Public Health Agency of Canada (2021). Interactive data visualizations of COVID-19 around the world [Dataset]. https://ouvert.canada.ca/data/dataset/fc11aa70-821b-4c64-be19-020a2465b0de
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    html, csvAvailable download formats
    Dataset updated
    Sep 24, 2021
    Dataset provided by
    Public Health Agency of Canada
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Area covered
    World
    Description

    Interactive data map of COVID-19 cases around the world. Shows number of total cases and deaths by country over time, starting from December 31, 2019 to present time.

  7. d

    Data from: Visualizing the lagged connection between COVID-19 cases and...

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Nov 19, 2023
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    Testa, Christian C.; Krieger, Nancy; Chen, Jarvis T.; Hanage, William P. (2023). Visualizing the lagged connection between COVID-19 cases and deaths in the United States: An animation using per capita state-level data (January 22, 2020 – July 8, 2020) [Dataset]. http://doi.org/10.7910/DVN/0C3BTS
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    Dataset updated
    Nov 19, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Testa, Christian C.; Krieger, Nancy; Chen, Jarvis T.; Hanage, William P.
    Description

    Data visualizations of the COVID-19 pandemic in the United States often have presented case and death rates by state in separate visualizations making it difficult to discern the temporal relationship between these two epidemiological metrics. By combining the COVID-19 case and death rates into a single visualization we have provided an intuitive format for depicting the relationship between cases and deaths. Moreover, by using animation we have made the temporal lag between cases and subsequent deaths more obvious and apparent. This work helps to inform expectations for the trajectory of death rates in the United States given the recent surge in case rates.

  8. USA COVID DATASET

    • kaggle.com
    Updated Nov 8, 2021
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    Ram Jas (2021). USA COVID DATASET [Dataset]. https://www.kaggle.com/datasets/ramjasmaurya/usa-covid-dataset
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 8, 2021
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Ram Jas
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Area covered
    United States
    Description

    USA covid 19 dataset describes the situation of the country from early covid till early march of 2021.

    the dataset comprises 17 columns and a huge number of rows.

    check out the whole dataset and do whatever you want to.

  9. COVID-19 Case Surveillance Public Use Data

    • data.cdc.gov
    • healthdata.gov
    • +5more
    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 [Dataset]. https://data.cdc.gov/Case-Surveillance/COVID-19-Case-Surveillance-Public-Use-Data/vbim-akqf
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    application/rdfxml, tsv, csv, json, xml, application/rssxmlAvailable 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 12 elements for all COVID-19 cases shared with CDC and includes demographics, any exposure history, disease severity indicators and outcomes, presence of any underlying medical conditions and risk behaviors, and no geographic data.

    CDC has three COVID-19 case surveillance datasets:

    The following apply to all three datasets:

    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.

    The deidentified data in the “COVID-19 Case Surveillance Public Use Data” include demographic characteristics, any exposure history, disease severity indicators and outcomes, clinical data, laboratory diagnostic test results, and presence of any underlying medical conditions and risk behaviors. All data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.

    COVID-19 Case Reports

    COVID-19 case reports have been routinely submitted using nationally standardized case reporting forms. On April 5, 2020, CSTE released an Interim Position Statement with national surveillance case definitions for COVID-19 included. Current versions of these case definitions are available here: 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 laboratory-confirmed or probable cases. On May 5, 2020, the standardized case reporting form was revised. Case reporting using this new form is ongoing among U.S. states and territories.

    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 and 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 (<5) records and indirect identifiers (e.g., date of first positive specimen). Suppression includes rare combinations of demographic characteristics (sex, age group, race/ethnicity). Suppressed values are re-coded to the NA answer option; records with data suppression are never removed.

    For questions, please contact Ask SRRG (eocevent394@cdc.gov).

    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

  10. f

    Independent Data Aggregation, Quality Control and Visualization of...

    • arizona.figshare.com
    png
    Updated May 30, 2023
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    Chun Ly; Jill McCleary; Cheryl Knott; Santiago Castiello-Gutiérrez (2023). Independent Data Aggregation, Quality Control and Visualization of University of Arizona COVID-19 Re-Entry Testing Data [Dataset]. http://doi.org/10.25422/azu.data.12966581.v2
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    pngAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    University of Arizona Research Data Repository
    Authors
    Chun Ly; Jill McCleary; Cheryl Knott; Santiago Castiello-Gutiérrez
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    AbstractThe dataset provided here contains the efforts of independent data aggregation, quality control, and visualization of the University of Arizona (UofA) COVID-19 testing programs for the 2019 novel Coronavirus pandemic. The dataset is provided in the form of machine-readable tables in comma-separated value (.csv) and Microsoft Excel (.xlsx) formats.Additional InformationAs part of the UofA response to the 2019-20 Coronavirus pandemic, testing was conducted on students, staff, and faculty prior to start of the academic year and throughout the school year. These testings were done at the UofA Campus Health Center and through their instance program called "Test All Test Smart" (TATS). These tests identify active cases of SARS-nCoV-2 infections using the reverse transcription polymerase chain reaction (RT-PCR) test and the Antigen test. Because the Antigen test provided more rapid diagnosis, it was greatly used three weeks prior to the start of the Fall semester and throughout the academic year.As these tests were occurring, results were provided on the COVID-19 websites. First, beginning in early March, the Campus Health Alerts website reported the total number of positive cases. Later, numbers were provided for the total number of tests (March 12 and thereafter). According to the website, these numbers were updated daily for positive cases and weekly for total tests. These numbers were reported until early September where they were then included in the reporting for the TATS program.For the TATS program, numbers were provided through the UofA COVID-19 Update website. Initially on August 21, the numbers provided were the total number (July 31 and thereafter) of tests and positive cases. Later (August 25), additional information was provided where both PCR and Antigen testings were available. Here, the daily numbers were also included. On September 3, this website then provided both the Campus Health and TATS data. Here, PCR and Antigen were combined and referred to as "Total", and daily and cumulative numbers were provided.At this time, no official data dashboard was available until September 16, and aside from the information provided on these websites, the full dataset was not made publicly available. As such, the authors of this dataset independently aggregated data from multiple sources. These data were made publicly available through a Google Sheet with graphical illustration provided through the spreadsheet and on social media. The goal of providing the data and illustrations publicly was to provide factual information and to understand the infection rate of SARS-nCoV-2 in the UofA community.Because of differences in reported data between Campus Health and the TATS program, the dataset provides Campus Health numbers on September 3 and thereafter. TATS numbers are provided beginning on August 14, 2020.Description of Dataset ContentThe following terms are used in describing the dataset.1. "Report Date" is the date and time in which the website was updated to reflect the new numbers2. "Test Date" is to the date of testing/sample collection3. "Total" is the combination of Campus Health and TATS numbers4. "Daily" is to the new data associated with the Test Date5. "To Date (07/31--)" provides the cumulative numbers from 07/31 and thereafter6. "Sources" provides the source of information. The number prior to the colon refers to the number of sources. Here, "UACU" refers to the UA COVID-19 Update page, and "UARB" refers to the UA Weekly Re-Entry Briefing. "SS" and "WBM" refers to screenshot (manually acquired) and "Wayback Machine" (see Reference section for links) with initials provided to indicate which author recorded the values. These screenshots are available in the records.zip file.The dataset is distinguished where available by the testing program and the methods of testing. Where data are not available, calculations are made to fill in missing data (e.g., extrapolating backwards on the total number of tests based on daily numbers that are deemed reliable). Where errors are found (by comparing to previous numbers), those are reported on the above Google Sheet with specifics noted.For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu

  11. Covid_19

    • kaggle.com
    zip
    Updated May 13, 2021
    + more versions
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    Dhrumil Gohel (2021). Covid_19 [Dataset]. https://www.kaggle.com/datasets/dhrumilgohel/covid-19
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    zip(571631 bytes)Available download formats
    Dataset updated
    May 13, 2021
    Authors
    Dhrumil Gohel
    Description

    Dataset

    This dataset was created by Dhrumil Gohel

    Contents

  12. COVID-19 Case Surveillance Restricted Access Detailed Data

    • data.cdc.gov
    • healthdata.gov
    • +1more
    application/rdfxml +5
    Updated Nov 20, 2020
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    CDC Data, Analytics and Visualization Task Force (2020). COVID-19 Case Surveillance Restricted Access Detailed Data [Dataset]. https://data.cdc.gov/Case-Surveillance/COVID-19-Case-Surveillance-Restricted-Access-Detai/mbd7-r32t
    Explore at:
    application/rssxml, xml, json, csv, tsv, application/rdfxmlAvailable download formats
    Dataset updated
    Nov 20, 2020
    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 publicly available dataset has 33 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. This dataset requires a registration process and a data use agreement.

    CDC has three COVID-19 case surveillance datasets:

    Requesting Access to the COVID-19 Case Surveillance Restricted Access Detailed Data Please review the following documents to determine your interest in accessing the COVID-19 Case Surveillance Restricted Access Detailed Data file: 1) CDC COVID-19 Case Surveillance Restricted Access Detailed Data: Summary, Guidance, Limitations Information, and Restricted Access Data Use Agreement Information 2) Data Dictionary for the COVID-19 Case Surveillance Restricted Access Detailed Data The next step is to complete the Registration Information and Data Use Restrictions Agreement (RIDURA). Once complete, CDC will review your agreement. After access is granted, Ask SRRG (eocevent394@cdc.gov) will email you information about how to access the data through GitHub. If you have questions about obtaining access, email eocevent394@cdc.gov.

    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.

    COVID-19 case surveillance data are collected by jurisdictions and are shared voluntarily with CDC. For more information, visit: https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.

    The deidentified data in the restricted access dataset include demographic characteristics, state and county of residence, any exposure history, disease severity indicators and outcomes, clinical data, laboratory diagnostic test results, and comorbidities.

    All data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.

    COVID-19 case reports have been routinely submitted using standardized case reporting forms.

    On April 5, 2020, CSTE released an Interim Position Statement with national surveillance case definitions for COVID-19 included. Current versions of these case definitions are available here: https://ndc.services.cdc.gov/case-definitions/coronavirus-disease-2019-2021/.

    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. 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. Case reporting using this new form is ongoing among U.S. states and territories.

    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:

  13. o

    COVID-19 Twitter Engagement Data

    • opendatabay.com
    .undefined
    Updated Jul 8, 2025
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    Datasimple (2025). COVID-19 Twitter Engagement Data [Dataset]. https://www.opendatabay.com/data/web-social/222b5de3-34ba-460d-918b-d917fc82b075
    Explore at:
    .undefinedAvailable download formats
    Dataset updated
    Jul 8, 2025
    Dataset authored and provided by
    Datasimple
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    Data Science and Analytics
    Description

    This dataset focuses on Twitter engagement metrics related to the Coronavirus disease (COVID-19), an infectious disease caused by the SARS-CoV-2 virus [1]. It provides a detailed collection of tweets, including their text content, the accounts that posted them, any hashtags used, and the geographical locations associated with the accounts [1]. The dataset is valuable for understanding public discourse, information dissemination, and engagement patterns on Twitter concerning COVID-19, particularly for analysing how people experience mild to moderate symptoms and recover, or require medical attention [1].

    Columns

    • Datetime: Represents the exact date and time a tweet was posted [2].
    • Tweet Id: A unique identifier assigned to each tweet [2].
    • Text: The actual content of the tweet [2].
    • Username: The display name of the tweet author [2].
    • Permalink: The direct link to the tweet on Twitter [2].
    • User: A link to the author's Twitter account [2].
    • Outlinks: Any external links included within the tweet [2].
    • CountLinks: The number of links present in the tweet [2].
    • ReplyCount: The total number of replies to that specific tweet [2].
    • RetweetCount: The total number of retweets of that specific tweet [2].
    • DateTime Count: A daily count of tweets, aggregated by date ranges [2].
    • Label Count: A count associated with specific ranges of tweet IDs or other engagement metrics, indicating the distribution of tweets within those ranges [3-5].

    Distribution

    The dataset is structured with daily tweet counts and covers a period from 10 January 2020 to 28 February 2020 [2, 6, 7]. It includes approximately 179,040 daily tweet entries during this timeframe, derived from the sum of daily counts and tweet ID counts [2, 3, 6-11]. Tweet activity shows distinct peaks, with notable increases in late January (e.g., 6,091 tweets between 23-24 January 2020) [2] and a significant surge in late February, reaching 47,643 tweets between 26-27 February 2020, followed by 42,289 and 44,824 in subsequent days [7, 10, 11]. The distribution of certain tweet engagement metrics, such as replies or retweets, indicates that a substantial majority of tweets (over 152,500 records) fall within lower engagement ranges (e.g., 0-43 or 0-1628.96), with fewer tweets showing very high engagement (e.g., only 1 record between 79819.04-81448.00) [4, 5]. The data file would typically be in CSV format [12].

    Usage

    This dataset is ideal for: * Data Science and Analytics projects focused on social media [1]. * Visualization of tweet trends and engagement over time. * Exploratory data analysis to uncover patterns in COVID-19 related discussions [1]. * Natural Language Processing (NLP) tasks, such as sentiment analysis or topic modelling on tweet content [1]. * Data cleaning and preparation exercises for social media data [1].

    Coverage

    The dataset has a global geographic scope [13]. It covers tweet data from 10 January 2020 to 28 February 2020 [2, 6, 7]. The content is specific to the Coronavirus disease (COVID-19) [1].

    License

    CC0

    Who Can Use It

    This dataset is particularly useful for: * Data scientists and analysts interested in social media trends and public health discourse [1]. * Researchers studying information spread and public sentiment during health crises. * Developers building AI and LLM data solutions [13]. * Individuals interested in exploratory analysis and data visualization of real-world social media data [1].

    Dataset Name Suggestions

    • COVID-19 Twitter Engagement Data
    • SARS-CoV-2 Tweet Activity Log
    • Pandemic Social Media Discourse
    • Coronavirus Tweets Analytics
    • Global COVID-19 Tweet Metrics

    Attributes

    Original Data Source: Covid_19 Tweets Dataset

  14. Z

    Mapping the COVID-19 global response: from grassroots to governments

    • data.niaid.nih.gov
    Updated Jul 22, 2024
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    Akligoh, Harry (2024). Mapping the COVID-19 global response: from grassroots to governments [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_3732376
    Explore at:
    Dataset updated
    Jul 22, 2024
    Dataset provided by
    Havemann, Jo
    Restrepo, Martin
    Akligoh, Harry
    Obanda, Johanssen
    Description

    Visual map at kumu.io/access2perspectives/covid19-resources

    Data set doi: 10.5281/zenodo.3732377 // available in different formats (pdf, xls, ods, csv,)

    Correspondence: (JH) info@access2perspectives.com

    Objectives

    Provide citizens with crucial and reliable information

    Encourage and facilitate South South collaboration

    Bridging language barriers

    Provide local governments and cities with lessons learned about COVID-19 crisis response

    Facilitate global cooperation and immediate response on all societal levels

    Enable LMICs to collaborate and innovate across distances and leverage locally available and context-relevant resources

    Methodology

    The data feeding the map at kumu.io was compiled from online resources and information shared in various community communication channels.

    Kumu.io is a visualization platform for mapping complex systems and to provide a deeper understanding of their intrinsic relationships. It provides blended systems thinking, stakeholder mapping, and social network analysis.

    Explore the map // https://kumu.io/access2perspectives/covid19-resources#global

    Click on individual nodes and view the information by country

    info hotlines

    governmental informational websites, Twitter feeds & Facebook pages

    fact checking online resources

    language indicator

    DIY resources

    clinical staff capacity building

    etc.

    With the navigation buttons to the right, you can zoom in and out, select and focus on specific elements.

    If you have comments, questions or suggestions for improvements on this map email us at info@access2perspectives.com

    Contribute

    Please add data to the spreadsheet at https://tinyurl.com/COVID19-global-response

    you can add additional information on country, city or neighbourhood level (see e.g. the Cape Town entry)

    Related documents

    Google Doc: tinyurl.com/COVID19-Africa-Response

  15. M

    US Coronavirus Cases & Deaths by State: Track COVID-19 data daily by state...

    • catalog.midasnetwork.us
    csv
    Updated Jul 12, 2023
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    MIDAS Coordination Center (2023). US Coronavirus Cases & Deaths by State: Track COVID-19 data daily by state and county [Dataset]. https://catalog.midasnetwork.us/collection/275
    Explore at:
    csvAvailable download formats
    Dataset updated
    Jul 12, 2023
    Dataset authored and provided by
    MIDAS Coordination Center
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Area covered
    United States
    Variables measured
    disease, COVID-19, pathogen, case counts, Homo sapiens, host organism, mortality data, infectious disease, Severe acute respiratory syndrome coronavirus 2
    Dataset funded by
    National Institute of General Medical Sciences
    Description

    The dataset data visualization contains information on where COVID-19 is spreading by tracking new cases found each day and the total number of cases and deaths in the US on the county-level. The data can be dowloaded and visualized on the website.

  16. A Personalized Activity-based Spatiotemporal Risk Mapping Approach to...

    • figshare.com
    tiff
    Updated Mar 18, 2021
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    Jing Li; Xuantong Wang; Hexuan Zheng; Tong Zhang (2021). A Personalized Activity-based Spatiotemporal Risk Mapping Approach to COVID-19 Pandemic [Dataset]. http://doi.org/10.6084/m9.figshare.13517105.v1
    Explore at:
    tiffAvailable download formats
    Dataset updated
    Mar 18, 2021
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Jing Li; Xuantong Wang; Hexuan Zheng; Tong Zhang
    License

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

    Description

    The datasets used for this manuscript were derived from multiple sources: Denver Public Health, Esri, Google, and SafeGraph. Any reuse or redistribution of the datasets are subjected to the restrictions of the data providers: Denver Public Health, Esri, Google, and SafeGraph and should consult relevant parties for permissions.1. COVID-19 case dataset were retrieved from Denver Public Health (Link: https://storymaps.arcgis.com/stories/50dbb5e7dfb6495292b71b7d8df56d0a )2. Point of Interests (POIs) data were retrieved from Esri and SafeGraph (Link: https://coronavirus-disasterresponse.hub.arcgis.com/datasets/6c8c635b1ea94001a52bf28179d1e32b/data?selectedAttribute=naics_code) and verified with Google Places Service (Link: https://developers.google.com/maps/documentation/javascript/reference/places-service)3. The activity risk information is accessible from Texas Medical Association (TMA) (Link: https://www.texmed.org/TexasMedicineDetail.aspx?id=54216 )The datasets for risk assessment and mapping are included in a geodatabase. Per SafeGraph data sharing guidelines, raw data cannot be shared publicly. To view the content of the geodatabase, users should have installed ArcGIS Pro 2.7. The geodatabase includes the following:1. POI. Major attributes are locations, name, and daily popularity.2. Denver neighborhood with weekly COVID-19 cases and computed regional risk levels.3. Simulated four travel logs with anchor points provided. Each is a separate point layer.

  17. COVID-19 Time-Series Metrics by County and State

    • kaggle.com
    Updated Sep 26, 2021
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    Yasir Hussein Shakir (2021). COVID-19 Time-Series Metrics by County and State [Dataset]. https://www.kaggle.com/yasserhessein/covid19-timeseries-metrics-by-county-and-state/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 26, 2021
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Yasir Hussein Shakir
    Description

    Context

    COVID-19 Time-Series Metrics by County and State Data is from the California COVID-19 State Dashboard at https://covid19.ca.gov/state-dashboard/

    https://i0.wp.com/post.healthline.com/wp-content/uploads/2021/08/doctor-patient-covid-hospital-1296x728-header.jpg?w=1155&h=1528">

    Content

    • Statewide COVID-19 Cases Deaths TestsCSV Popular Statewide COVID-19 Cases Deaths Tests

    • Statewide COVID-19 Cases Deaths DemographicsCSV Popular State COVID-19 Cases Deaths Demographics

    • All resource dataZIP

    • COVID-19 Cases Deaths Tests Data DictionaryXLSX Popular

    • COVID-19 Cases Deaths Demographics Data DictionaryXLSX Popular

    Program Contact Name

    COVID-19 Response Data, Informatics, Surveillance, Clinical and Outbreaks (DISCO) Team

    Program Contact Email : opendata@cdph.ca.gov

    Source Link : https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/Immunization/ncov2019.aspx

    Last Updated : September 27, 2021, 1:00 AM (UTC+08:00)

    Created : April 7, 2021, 8:18 AM (UTC+08:00)

    More Source : https://data.ca.gov/dataset/covid-19-time-series-metrics-by-county-and-state1

  18. COVID-19 DATA [COUNTY,STATE,DEATHS,CONFIRMED CASE]

    • kaggle.com
    Updated May 22, 2020
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    Pavithra T (2020). COVID-19 DATA [COUNTY,STATE,DEATHS,CONFIRMED CASE] [Dataset]. https://www.kaggle.com/pavithrat27/covid19-data-countystatedeathsconfirmed-case/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 22, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Pavithra T
    Description

    Context

    The DATESET is of US-COUNTRIES for COVID19.

    Description

    1. Covid_Data based on each countystates.csv= Contains Deaths,confirmed_cases,state,county 2.Covid_Data= Contains state,county,country,zipcode,city,Covidimpacted,latitude,longitude,timezone

    Prediction can be done for column CovidImpacted by choosing Deaths,confirmed cases by some algo and show the accuracy,performance etc

    Content

    • The DATASET has city,state,county,Deaths,Confirmed_cases,latitude,longitude,zipcode.
    • DATASET can be used to classification based on cases/Deaths
    • DATA Analysis,DATA VISUALISATION can be done for DATASET.

    Inspiration

    As because we are in COVID19 hope this DATA can be used for beginners,intermediate to work in it Hope it Helps!

  19. d

    Johns Hopkins COVID-19 Case Tracker

    • data.world
    csv, zip
    Updated Jul 12, 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
    Explore at:
    zip, csvAvailable download formats
    Dataset updated
    Jul 12, 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

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

    • data.cdc.gov
    • data.virginia.gov
    • +3more
    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
    Explore at:
    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

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Agency for Healthcare Research and Quality, Department of Health & Human Services (2023). HCUP Visualization of Inpatient Trends in COVID-19 and Other Conditions [Dataset]. https://catalog.data.gov/dataset/hcup-visualization-of-inpatient-trends-in-covid-19-and-other-conditions
Organization logoOrganization logo

HCUP Visualization of Inpatient Trends in COVID-19 and Other Conditions

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11 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Jul 26, 2023
Description

The HCUP Visualization of Inpatient Trends in COVID-19 and Other Conditions displays State-specific monthly trends in inpatient stays related to COVID-19 and other conditions, and facilitates comparisons of the number of hospital discharges, the average length of stays, and in-hospital mortality rates across patient/stay characteristics and States. This information is based on the HCUP State Inpatient Databases (SID), starting with 2018 data, plus newer annual and quarterly inpatient data, if and when available.

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