100+ datasets found
  1. COVID-19 Dataset

    • kaggle.com
    zip
    Updated Nov 13, 2022
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    Meir Nizri (2022). COVID-19 Dataset [Dataset]. https://www.kaggle.com/datasets/meirnizri/covid19-dataset
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    zip(4890659 bytes)Available download formats
    Dataset updated
    Nov 13, 2022
    Authors
    Meir Nizri
    License

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

    Description

    Context

    Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people infected with COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. Older people, and those with underlying medical problems like cardiovascular disease, diabetes, chronic respiratory disease, and cancer are more likely to develop serious illness. During the entire course of the pandemic, one of the main problems that healthcare providers have faced is the shortage of medical resources and a proper plan to efficiently distribute them. In these tough times, being able to predict what kind of resource an individual might require at the time of being tested positive or even before that will be of immense help to the authorities as they would be able to procure and arrange for the resources necessary to save the life of that patient.

    The main goal of this project is to build a machine learning model that, given a Covid-19 patient's current symptom, status, and medical history, will predict whether the patient is in high risk or not.

    content

    The dataset was provided by the Mexican government (link). This dataset contains an enormous number of anonymized patient-related information including pre-conditions. The raw dataset consists of 21 unique features and 1,048,576 unique patients. In the Boolean features, 1 means "yes" and 2 means "no". values as 97 and 99 are missing data.

    • sex: 1 for female and 2 for male.
    • age: of the patient.
    • classification: covid test findings. Values 1-3 mean that the patient was diagnosed with covid in different degrees. 4 or higher means that the patient is not a carrier of covid or that the test is inconclusive.
    • patient type: type of care the patient received in the unit. 1 for returned home and 2 for hospitalization.
    • pneumonia: whether the patient already have air sacs inflammation or not.
    • pregnancy: whether the patient is pregnant or not.
    • diabetes: whether the patient has diabetes or not.
    • copd: Indicates whether the patient has Chronic obstructive pulmonary disease or not.
    • asthma: whether the patient has asthma or not.
    • inmsupr: whether the patient is immunosuppressed or not.
    • hypertension: whether the patient has hypertension or not.
    • cardiovascular: whether the patient has heart or blood vessels related disease.
    • renal chronic: whether the patient has chronic renal disease or not.
    • other disease: whether the patient has other disease or not.
    • obesity: whether the patient is obese or not.
    • tobacco: whether the patient is a tobacco user.
    • usmr: Indicates whether the patient treated medical units of the first, second or third level.
    • medical unit: type of institution of the National Health System that provided the care.
    • intubed: whether the patient was connected to the ventilator.
    • icu: Indicates whether the patient had been admitted to an Intensive Care Unit.
    • date died: If the patient died indicate the date of death, and 9999-99-99 otherwise.
  2. g

    Coronavirus (Covid-19) Data in the United States

    • github.com
    • openicpsr.org
    • +2more
    csv
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    New York Times, Coronavirus (Covid-19) Data in the United States [Dataset]. https://github.com/nytimes/covid-19-data
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    csvAvailable download formats
    Dataset provided by
    New York Times
    License

    https://github.com/nytimes/covid-19-data/blob/master/LICENSEhttps://github.com/nytimes/covid-19-data/blob/master/LICENSE

    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 the first reported coronavirus case in Washington State on Jan. 21, 2020, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

    We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

    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. Chicago COVID-19 Dataset

    • kaggle.com
    zip
    Updated Jul 20, 2022
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    Ryan Park (2022). Chicago COVID-19 Dataset [Dataset]. https://www.kaggle.com/datasets/ryandpark/chicago-covid19-dataset
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    zip(12812 bytes)Available download formats
    Dataset updated
    Jul 20, 2022
    Authors
    Ryan Park
    License

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

    Area covered
    Chicago
    Description

    Description Source data: https://www.chicago.gov/city/en/sites/covid-19/home/latest-data.html.

    Only Chicago residents are included based on the home ZIP Code as provided by the medical provider. If a ZIP was missing or was not valid, it is displayed as "Unknown".

    Cases with a positive molecular (PCR) or antigen test are included in this dataset. Cases are counted based on the week the test specimen was collected. For privacy reasons, until a ZIP Code reaches five cumulative cases, both the weekly and cumulative case counts will be blank. Therefore, summing the “Cases - Weekly” column is not a reliable way to determine case totals. Deaths are those that have occurred among cases based on the week of death.

    For tests, each test is counted once, based on the week the test specimen was collected. Tests performed prior to 3/1/2020 are not included. Test counts include multiple tests for the same person (a change made on 10/29/2020). PCR and antigen tests reported to Chicago Department of Public Health (CDPH) through electronic lab reporting are included. Electronic lab reporting has taken time to onboard and testing availability has shifted over time, so these counts are likely an underestimate of community infection.

    The “Percent Tested Positive” columns are calculated by dividing the number of positive tests by the number of total tests . Because of the data limitations for the Tests columns, such as persons being tested multiple times as a requirement for employment, these percentages may vary in either direction from the actual disease prevalence in the ZIP Code.

    All data are provisional and subject to change. Information is updated as additional details are received.

    To compare ZIP Codes to Chicago Community Areas, please see http://data.cmap.illinois.gov/opendata/uploads/CKAN/NONCENSUS/ADMINISTRATIVE_POLITICAL_BOUNDARIES/CCAzip.pdf. Both ZIP Codes and Community Areas are also geographic datasets on this data portal.

    Data Source: Illinois National Electronic Disease Surveillance System, Cook County Medical Examiner’s Office, Illinois Vital Records, American Community Survey (2018)

  4. WHO COVID-19 Global Data Insights

    • kaggle.com
    zip
    Updated Sep 30, 2023
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    Mohammad Reza Ghazi Manas (2023). WHO COVID-19 Global Data Insights [Dataset]. https://www.kaggle.com/datasets/mohammadrezagim/who-covid-19-global-data
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    zip(2309669 bytes)Available download formats
    Dataset updated
    Sep 30, 2023
    Authors
    Mohammad Reza Ghazi Manas
    License

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

    Description

    About Dataset: WHO COVID-19 Global Data

    This dataset provides comprehensive information on the global COVID-19 pandemic as reported to the World Health Organization (WHO). The dataset is available in comma-separated values (CSV) format and includes the following fields:

    Daily cases and deaths by date reported to WHO: WHO-COVID-19-global-data.csv

    • Date_reported (Date): The date of reporting to WHO.
    • Country_code (String): The ISO Alpha-2 country code.
    • Country (String): The name of the country, territory, or area.
    • WHO_region (String): The WHO regional office to which the country belongs. WHO Member States are grouped into six WHO regions, including AFRO (Regional Office for Africa), AMRO (Regional Office for the Americas), SEARO (Regional Office for South-East Asia), EURO (Regional Office for Europe), EMRO (Regional Office for the Eastern Mediterranean), and WPRO (Regional Office for the Western Pacific).
    • New_cases (Integer): The number of new confirmed cases reported on a given day. This is calculated by subtracting the previous cumulative case count from the current cumulative case count.
    • Cumulative_cases (Integer): The total cumulative confirmed cases reported to WHO up to the specified date.
    • New_deaths (Integer): The number of new confirmed deaths reported on a given day. Similar to new cases, this is calculated by subtracting the previous cumulative death count from the current cumulative death count.- Cumulative_deaths (Integer): The total cumulative confirmed deaths reported to WHO up to the specified date.

    In addition to the COVID-19 case and death data, this dataset also includes valuable information related to COVID-19 vaccinations. The vaccination data consists of the following fields:

    Vaccination Data Fields: vaccination-data.csv

    • COUNTRY (String): Country, territory, or area.
    • ISO3 (String): ISO Alpha-3 country code.
    • WHO_REGION (String): The WHO regional office to which the country belongs.
    • DATA_SOURCE (String): Indicates the data source, which can be either "REPORTING" (Data reported by Member States or sourced from official reports) or "OWID" (Data sourced from Our World in Data COVID-19 Vaccinations).
    • DATE_UPDATED (Date): Date of the last update.
    • TOTAL_VACCINATIONS (Integer): Cumulative total vaccine doses administered.
    • PERSONS_VACCINATED_1PLUS_DOSE (Decimal): Cumulative number of persons vaccinated with at least one dose.
    • TOTAL_VACCINATIONS_PER100 (Integer): Cumulative total vaccine doses administered per 100 population.
    • PERSONS_VACCINATED_1PLUS_DOSE_PER100 (Decimal): Cumulative persons vaccinated with at least one dose per 100 population.
    • PERSONS_LAST_DOSE (Integer): Cumulative number of persons vaccinated with a complete primary series.
    • PERSONS_LAST_DOSE_PER100 (Decimal): Cumulative number of persons vaccinated with a complete primary series per 100 population.
    • VACCINES_USED (String): Combined short name of the vaccine in the format "Company - Product name."
    • FIRST_VACCINE_DATE (Date): Date of the first vaccinations, equivalent to the start/launch date of the first vaccine administered in a country.
    • NUMBER_VACCINES_TYPES_USED (Integer): Number of vaccine types used per country, territory, or area.
    • PERSONS_BOOSTER_ADD_DOSE (Integer): Cumulative number of persons vaccinated with at least one booster or additional dose.
    • PERSONS_BOOSTER_ADD_DOSE_PER100 (Decimal): Cumulative number of persons vaccinated with at least one booster or additional dose per 100 population.

    In addition to the vaccination data, a separate dataset containing vaccination metadata is available, including information about vaccine names, product names, company names, authorization dates, start and end dates of vaccine rollout, and more.

    Vaccination metadata Fields: vaccination-metadata.csv

    • ISO3 (String): ISO Alpha-3 country code
    • VACCINE_NAME (String): Combined short name of vaccine: "Company - Product name" (see below)
    • PRODUCT_NAME (String): Name or label of vaccine product, or type of vaccine (if unnamed).
    • COMPANY_NAME (String): Marketing authorization holder of vaccine product.
    • FIRST_VACCINE_DATE (Date): Date of first vaccinations. Equivalent to start/launch date of the first vaccine administered in a country.
    • AUTHORIZATION_DATE (Date): Date vaccine product was authorized for use in the country, territory, area.
    • START_DATE (Date): Start/launch date of vaccination with vaccine type (excludes vaccinations during clinical trials).
    • END_DATE (Date): End date of vaccine rollout
    • COMMENT (String): Comments related to vaccine rollout
    • DATA_SOURCE (String): Indicates data source - REPORTING: Data reported by Member States, or sourced from official re...
  5. d

    COVID-19 County Level Data - Archive

    • catalog.data.gov
    • data.ct.gov
    • +1more
    Updated Jun 21, 2025
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    data.ct.gov (2025). COVID-19 County Level Data - Archive [Dataset]. https://catalog.data.gov/dataset/covid-19-county-level-data
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    Dataset updated
    Jun 21, 2025
    Dataset provided by
    data.ct.gov
    Description

    Covid-19 Daily metrics at the county level As of 6/1/2023, this data set is no longer being updated. The COVID-19 Data Report is posted on the Open Data Portal every day at 3pm. The report uses data from multiple sources, including external partners; if data from external partners are not received by 3pm, they are not available for inclusion in the report and will not be displayed. Data that are received after 3pm will still be incorporated and published in the next report update. The cumulative number of COVID-19 cases (cumulative_cases) includes all cases of COVID-19 that have ever been reported to DPH. The cumulative number of COVID_19 cases in the last 7 days (cases_7days) only includes cases where the specimen collection date is within the past 7 days. While most cases are reported to DPH within 48 hours of specimen collection, there are a small number of cases that routinely are delayed, and will have specimen collection dates that fall outside of the rolling 7 day reporting window. Additionally, reporting entities may submit correction files to contribute historic data during initial onboarding or to address data quality issues; while this is rare, these correction files may cause a large amount of data from outside of the current reporting window to be uploaded in a single day; this would result in the change in cumulative_cases being much larger than the value of cases_7days. On June 4, 2020, the US Department of Health and Human Services issued guidance requiring the reporting of positive and negative test results for SARS-CoV-2; this guidance expired with the end of the federal PHE on 5/11/2023, and negative SARS-CoV-2 results were removed from the List of Reportable Laboratory Findings. DPH will no longer be reporting metrics that were dependent on the collection of negative test results, specifically total tests performed or percent positivity. Positive antigen and PCR/NAAT results will continue to be reportable.

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

    • catalog.data.gov
    • data.virginia.gov
    • +5more
    Updated May 8, 2021
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    Centers for Disease Control and Prevention (2021). COVID-19 Case Surveillance Public Use Data with Geography [Dataset]. https://catalog.data.gov/dataset/covid-19-case-surveillance-public-use-data-with-geography-0605b
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    Dataset updated
    May 8, 2021
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    Description

    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 32 data element restricted access dataset. The following apply to the public use datasets and the restricted access dataset: - Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf. - Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers. - Some data are suppressed to protect individual privacy. - Datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the previously updated datasets. This 14-day lag allows case reporting to be stabilized and ensure that time-dependent outcome data are accurately captured. - Datasets are updated monthly. - Datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy. - For more information about data collection and reporting, please see wwwn.cdc.gov/nndss/data-collection.html. - For more information about the COVID-19 case surveillance data, please see www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html. Overview The COVID-19 case surveillance database includes patient-level data reported by 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 collected by jurisdictions are shared voluntarily with CDC. For more information, visit: wwwn.cdc.gov/nndss/conditions/coronavirus-disease-2019-covid-19/case-definition/2020/08/05/. COVID-19 Case Reports COVID-19 case reports are routinely submitted to CDC by pu

  7. COVID-19 Global Dataset

    • kaggle.com
    zip
    Updated Jun 2, 2024
    + more versions
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    Saikat Sinha Ray (2024). COVID-19 Global Dataset [Dataset]. https://www.kaggle.com/datasets/saikatsray/covid-19-global-dataset
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    zip(542392 bytes)Available download formats
    Dataset updated
    Jun 2, 2024
    Authors
    Saikat Sinha Ray
    License

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

    Description

    Dataset

    This dataset was created by Saikat Sinha Ray

    Released under Apache 2.0

    Contents

  8. B

    COVID-19 Twitter Dataset

    • borealisdata.ca
    • figshare.com
    Updated Nov 10, 2020
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    Anatoliy Gruzd; Philip Mai (2020). COVID-19 Twitter Dataset [Dataset]. http://doi.org/10.5683/SP2/PXF2CU
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 10, 2020
    Dataset provided by
    Borealis
    Authors
    Anatoliy Gruzd; Philip Mai
    License

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

    Description

    The current dataset contains 237M Tweet IDs for Twitter posts that mentioned "COVID" as a keyword or as part of a hashtag (e.g., COVID-19, COVID19) between March and July of 2020. Sampling Method: hourly requests sent to Twitter Search API using Social Feed Manager, an open source software that harvests social media data and related content from Twitter and other platforms. NOTE: 1) In accordance with Twitter API Terms, only Tweet IDs are provided as part of this dataset. 2) To recollect tweets based on the list of Tweet IDs contained in these datasets, you will need to use tweet 'rehydration' programs like Hydrator (https://github.com/DocNow/hydrator) or Python library Twarc (https://github.com/DocNow/twarc). 3) This dataset, like most datasets collected via the Twitter Search API, is a sample of the available tweets on this topic and is not meant to be comprehensive. Some COVID-related tweets might not be included in the dataset either because the tweets were collected using a standardized but intermittent (hourly) sampling protocol or because tweets used hashtags/keywords other than COVID (e.g., Coronavirus or #nCoV). 4) To broaden this sample, consider comparing/merging this dataset with other COVID-19 related public datasets such as: https://github.com/thepanacealab/covid19_twitter https://ieee-dataport.org/open-access/corona-virus-covid-19-tweets-dataset https://github.com/echen102/COVID-19-TweetIDs

  9. i

    COVID-19 dataset 3 classes

    • ieee-dataport.org
    Updated Jul 1, 2020
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    Vaishnavi Jamdade (2020). COVID-19 dataset 3 classes [Dataset]. https://ieee-dataport.org/documents/covid-19-dataset-3-classes
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    Dataset updated
    Jul 1, 2020
    Authors
    Vaishnavi Jamdade
    License

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

    Description

    The rapid outbreak of COVID-19 due to the novel coronavirus SARS-COV-2 is the biggest issue faced by mankind today. It is important to detect the positive cases as early as possible to prevent the further spread of this pandemic.

  10. Public Health Infobase - Data on COVID-19 in Canada

    • open.canada.ca
    • datasets.ai
    csv
    Updated Nov 21, 2024
    + more versions
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    Public Health Agency of Canada (2024). Public Health Infobase - Data on COVID-19 in Canada [Dataset]. https://open.canada.ca/data/en/dataset/261c32ab-4cfd-4f81-9dea-7b64065690dc
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    csvAvailable download formats
    Dataset updated
    Nov 21, 2024
    Dataset provided by
    Public Health Agency Of Canadahttp://www.phac-aspc.gc.ca/
    License

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

    Area covered
    Canada
    Description

    The data contained in the table describes COVID-19 in Canada in terms of number of cases and deaths at the provincial and national levels from January 31, 2020 to present time. It also describes the number of tests performed and the number of people recovered. The values displayed in the table are provided by the Public Health Infobase, managed by the Health Promotion and Chronic Disease Prevention Branch (HPCDPB) of the Public Health Agency of Canada (PHAC). The values are updated daily.

  11. d

    COVID-19 Cases, Tests, and Deaths by ZIP Code - Historical

    • catalog.data.gov
    • data.cityofchicago.org
    • +2more
    Updated May 24, 2024
    + more versions
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    data.cityofchicago.org (2024). COVID-19 Cases, Tests, and Deaths by ZIP Code - Historical [Dataset]. https://catalog.data.gov/dataset/covid-19-cases-tests-and-deaths-by-zip-code
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    Dataset updated
    May 24, 2024
    Dataset provided by
    data.cityofchicago.org
    Description

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

  12. d

    COVID-19 Cases, Hospitalizations, and Deaths (By County) - ARCHIVE

    • catalog.data.gov
    • data.ct.gov
    Updated Aug 12, 2023
    + more versions
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    data.ct.gov (2023). COVID-19 Cases, Hospitalizations, and Deaths (By County) - ARCHIVE [Dataset]. https://catalog.data.gov/dataset/covid-19-cases-hospitalizations-and-deaths-by-county
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    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, hospitalizations, and associated deaths that have been reported among Connecticut residents. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Hospitalization data were collected by the Connecticut Hospital Association and reflect the number of patients currently hospitalized with laboratory-confirmed COVID-19. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the daily COVID-19 update. 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 examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More information on COVID-19 mortality can be found at the following link: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Mortality/Mortality-Statistics Data are reported d

  13. United States COVID-19 County Level of Community Transmission Historical...

    • data.cdc.gov
    • odgavaprod.ogopendata.com
    • +1more
    csv, xlsx, xml
    Updated Oct 21, 2022
    + more versions
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    CDC COVID-19 Response (2022). United States COVID-19 County Level of Community Transmission Historical Changes - ARCHIVED [Dataset]. https://data.cdc.gov/Public-Health-Surveillance/United-States-COVID-19-County-Level-of-Community-T/nra9-vzzn
    Explore at:
    xlsx, xml, csvAvailable download formats
    Dataset updated
    Oct 21, 2022
    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

    On October 20, 2022, CDC began retrieving aggregate case and death data from jurisdictional and state partners weekly instead of daily. This dataset contains archived historical community transmission and related data elements by county. Although these data will continue to be publicly available, this dataset has not been updated since October 20, 2022. An archived dataset containing weekly historical community transmission data by county can also be found here: Weekly COVID-19 County Level of Community Transmission Historical Changes | Data | Centers for Disease Control and Prevention (cdc.gov).

    Related data CDC has been providing the public with two versions of COVID-19 county-level community transmission level data: this historical dataset with the daily county-level transmission data from January 22, 2020, and a dataset with the daily values as originally posted on the COVID Data Tracker. Similar to this dataset, the original dataset with daily data as posted is archived on 10/20/2022. It will continue to be publicly available but will no longer be updated. A new dataset containing community transmission data by county as originally posted is now published weekly and can be found at: Weekly COVID-19 County Level of Community Transmission as Originally Posted | Data | Centers for Disease Control and Prevention (cdc.gov).

    This public use dataset has 7 data elements reflecting historical data for community transmission levels for all available counties and jurisdictions. It contains historical data for the county level of community transmission and includes updated data submitted by states and jurisdictions. Each day, the dataset was updated to include the most recent days’ data and incorporate any historical changes made by jurisdictions. This dataset includes data since January 22, 2020. Transmission level is set to low, moderate, substantial, or high using the calculation rules below.

    Methods for calculating county level of community transmission indicator The County Level of Community Transmission indicator uses two metrics: (1) total new COVID-19 cases per 100,000 persons in the last 7 days and (2) percentage of positive SARS-CoV-2 diagnostic nucleic acid amplification tests (NAAT) in the last 7 days. For each of these metrics, CDC classifies transmission values as low, moderate, substantial, or high (below and here). If the values for each of these two metrics differ (e.g., one indicates moderate and the other low), then the higher of the two should be used for decision-making.

    CDC core metrics of and thresholds for community transmission levels of SARS-CoV-2

    Total New Case Rate Metric: "New cases per 100,000 persons in the past 7 days" is calculated by adding the number of new cases in the county (or other administrative level) in the last 7 days divided by the population in the county (or other administrative level) and multiplying by 100,000. "New cases per 100,000 persons in the past 7 days" is considered to have transmission level of Low (0-9.99); Moderate (10.00-49.99); Substantial (50.00-99.99); and High (greater than or equal to 100.00).

    Test Percent Positivity Metric: "Percentage of positive NAAT in the past 7 days" is calculated by dividing the number of positive tests in the county (or other administrative level) during the last 7 days by the total number of tests resulted over the last 7 days. "Percentage of positive NAAT in the past 7 days" is considered to have transmission level of Low (less than 5.00); Moderate (5.00-7.99); Substantial (8.00-9.99); and High (greater than or equal to 10.00).

    If the two metrics suggest different transmission levels, the higher level is selected. If one metric is missing, the other metric is used for the indicator.

    The reported transmission categories include:

    Low Transmission Threshold: Counties with fewer than 10 total cases per 100,000 population in the past 7 days, and a NAAT percent test positivity in the past 7 days below 5%;

    Moderate Transmission Threshold: Counties with 10-49 total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 5.0-7.99%;

    Substantial Transmission Threshold: Counties with 50-99 total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 8.0-9.99%;

    High Transmission Threshold: Counties with 100 or more total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 10.0% or greater.

    Blank: total new cases in the past 7 days are not reported (county data known to be unavailable) and the percentage of positive NAATs tests during the past 7 days (blank) are not reported.

    Data Suppression To prevent the release of data that could be used to identify people, data cells are suppressed for low frequency. When the case counts used to calculate the total new case rate metric ("cases_per_100K_7_day_count_change") is greater than zero and less than 10, this metric is set to "suppressed" to protect individual privacy. If the case count is 0, the total new case rate metric is still displayed.

    The data in this dataset are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers. This datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access.

    Duplicate Records Issue A bug was found on 12/28/2021 that caused many records in the dataset to be duplicated. This issue was resolved on 01/06/2022.

  14. COVID-19 Case Surveillance Public Use Data

    • data.virginia.gov
    • catalog.midasnetwork.us
    • +7more
    csv, json, rdf, xsl
    Updated Feb 23, 2025
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    Centers for Disease Control and Prevention (2025). COVID-19 Case Surveillance Public Use Data [Dataset]. https://data.virginia.gov/dataset/covid-19-case-surveillance-public-use-data
    Explore at:
    csv, xsl, rdf, jsonAvailable download formats
    Dataset updated
    Feb 23, 2025
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    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 aut

  15. d

    Johns Hopkins COVID-19 Case Tracker

    • data.world
    • kaggle.com
    csv, zip
    Updated Nov 28, 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
    Nov 28, 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

  16. m

    COVID-19 reporting

    • mass.gov
    Updated Oct 21, 2022
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    Executive Office of Health and Human Services (2022). COVID-19 reporting [Dataset]. https://www.mass.gov/info-details/covid-19-reporting
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    Dataset updated
    Oct 21, 2022
    Dataset provided by
    Department of Public Health
    Executive Office of Health and Human Services
    Area covered
    Massachusetts
    Description

    The COVID-19 dashboard includes data on city/town COVID-19 activity, confirmed and probable cases of COVID-19, confirmed and probable deaths related to COVID-19, and the demographic characteristics of cases and deaths.

  17. COVID-19 Dataset: Global Data for Analysis

    • kaggle.com
    zip
    Updated Jul 9, 2023
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    Saunak Ghosh (2023). COVID-19 Dataset: Global Data for Analysis [Dataset]. https://www.kaggle.com/datasets/saunakghosh/covid-dataset-latest
    Explore at:
    zip(10941 bytes)Available download formats
    Dataset updated
    Jul 9, 2023
    Authors
    Saunak Ghosh
    Description

    Unlock the Insights: Dive into the Comprehensive COVID-19 Dataset and Uncover Key Patterns, Trends, and Impacts Worldwide. Empower Your Analysis with Rich, Reliable, and Up-to-Date Data.

    The COVID-19 dataset provides a comprehensive collection of information related to the global pandemic. It encompasses various aspects such as the number of cases, deaths, recoveries, testing, and more. The dataset serves as a valuable resource for researchers, analysts, and individuals seeking to understand the impact and spread of the virus.

    The dataset is compiled from reputable sources, including official government reports, health organizations, and reliable data repositories. It ensures the accuracy and reliability of the information, making it a trusted source for COVID-19 data analysis.

    The dataset is inspired by the need for reliable and up-to-date information about the COVID-19 pandemic. It aims to provide a comprehensive resource that enables researchers, analysts, and individuals to gain insights, track trends, and make data-driven decisions to combat the global health crisis.

    By leveraging this dataset, users can gain a deeper understanding of the pandemic's progression, identify patterns, and contribute to the ongoing efforts in managing and mitigating the impact of COVID-19.

  18. n

    COVID-19 Open Research Dataset

    • neuinfo.org
    • scicrunch.org
    • +2more
    Updated Aug 11, 2024
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    (2024). COVID-19 Open Research Dataset [Dataset]. http://identifiers.org/RRID:SCR_018336
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    Dataset updated
    Aug 11, 2024
    Description

    Collection of scholarly articles about COVID-19 and coronavirus family of viruses for use by global research community. Dataset is updated on weekly basis.

  19. United States COVID-19 Community Levels by County

    • datalumos.org
    • healthdata.gov
    • +2more
    delimited
    Updated Oct 16, 2025
    + more versions
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    United States Department of Health and Human Services. Centers for Disease Control and Prevention (2025). United States COVID-19 Community Levels by County [Dataset]. http://doi.org/10.3886/E238954V1
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    delimitedAvailable download formats
    Dataset updated
    Oct 16, 2025
    Authors
    United States Department of Health and Human Services. Centers for Disease Control and Prevention
    License

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

    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 reflect

  20. COVID-19 State Profile Report - Nevada

    • healthdata.gov
    • data.virginia.gov
    • +2more
    csv, xlsx, xml
    Updated Jan 27, 2021
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    White House COVID-19 Team, Joint Coordination Cell, Data Strategy and Execution Workgroup (2021). COVID-19 State Profile Report - Nevada [Dataset]. https://healthdata.gov/Community/COVID-19-State-Profile-Report-Nevada/ym2i-z2sf
    Explore at:
    csv, xlsx, xmlAvailable download formats
    Dataset updated
    Jan 27, 2021
    Dataset authored and provided by
    White House COVID-19 Team, Joint Coordination Cell, Data Strategy and Execution Workgroup
    License

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

    Area covered
    Nevada
    Description

    After over two years of public reporting, the State Profile Report will no longer be produced and distributed after February 2023. The final release was on February 23, 2023. We want to thank everyone who contributed to the design, production, and review of this report and we hope that it provided insight into the data trends throughout the COVID-19 pandemic. Data about COVID-19 will continue to be updated at CDC’s COVID Data Tracker.

    The State Profile Report (SPR) is generated by the Data Strategy and Execution Workgroup in the Joint Coordination Cell, in collaboration with the White House. It is managed by an interagency team with representatives from multiple agencies and offices (including the United States Department of Health and Human Services (HHS), the Centers for Disease Control and Prevention, the HHS Assistant Secretary for Preparedness and Response, and the Indian Health Service). The SPR provides easily interpretable information on key indicators for each state, down to the county level.

    It is a weekly snapshot in time that:

    • Focuses on recent outcomes in the last seven days and changes relative to the month prior
    • Provides additional contextual information at the county level for each state, and includes national level information
    • Supports rapid visual interpretation of results with color thresholds

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Meir Nizri (2022). COVID-19 Dataset [Dataset]. https://www.kaggle.com/datasets/meirnizri/covid19-dataset
Organization logo

COVID-19 Dataset

COVID-19 patient's symptoms, status, and medical history.

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28 scholarly articles cite this dataset (View in Google Scholar)
zip(4890659 bytes)Available download formats
Dataset updated
Nov 13, 2022
Authors
Meir Nizri
License

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

Description

Context

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people infected with COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. Older people, and those with underlying medical problems like cardiovascular disease, diabetes, chronic respiratory disease, and cancer are more likely to develop serious illness. During the entire course of the pandemic, one of the main problems that healthcare providers have faced is the shortage of medical resources and a proper plan to efficiently distribute them. In these tough times, being able to predict what kind of resource an individual might require at the time of being tested positive or even before that will be of immense help to the authorities as they would be able to procure and arrange for the resources necessary to save the life of that patient.

The main goal of this project is to build a machine learning model that, given a Covid-19 patient's current symptom, status, and medical history, will predict whether the patient is in high risk or not.

content

The dataset was provided by the Mexican government (link). This dataset contains an enormous number of anonymized patient-related information including pre-conditions. The raw dataset consists of 21 unique features and 1,048,576 unique patients. In the Boolean features, 1 means "yes" and 2 means "no". values as 97 and 99 are missing data.

  • sex: 1 for female and 2 for male.
  • age: of the patient.
  • classification: covid test findings. Values 1-3 mean that the patient was diagnosed with covid in different degrees. 4 or higher means that the patient is not a carrier of covid or that the test is inconclusive.
  • patient type: type of care the patient received in the unit. 1 for returned home and 2 for hospitalization.
  • pneumonia: whether the patient already have air sacs inflammation or not.
  • pregnancy: whether the patient is pregnant or not.
  • diabetes: whether the patient has diabetes or not.
  • copd: Indicates whether the patient has Chronic obstructive pulmonary disease or not.
  • asthma: whether the patient has asthma or not.
  • inmsupr: whether the patient is immunosuppressed or not.
  • hypertension: whether the patient has hypertension or not.
  • cardiovascular: whether the patient has heart or blood vessels related disease.
  • renal chronic: whether the patient has chronic renal disease or not.
  • other disease: whether the patient has other disease or not.
  • obesity: whether the patient is obese or not.
  • tobacco: whether the patient is a tobacco user.
  • usmr: Indicates whether the patient treated medical units of the first, second or third level.
  • medical unit: type of institution of the National Health System that provided the care.
  • intubed: whether the patient was connected to the ventilator.
  • icu: Indicates whether the patient had been admitted to an Intensive Care Unit.
  • date died: If the patient died indicate the date of death, and 9999-99-99 otherwise.
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