7 datasets found
  1. n

    Coronavirus (Covid-19) Data in the United States

    • nytimes.com
    • openicpsr.org
    • +4more
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    New York Times, Coronavirus (Covid-19) Data in the United States [Dataset]. https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html
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    Dataset provided by
    New York Times
    Description

    The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

    Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

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

    The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

  2. Covid Dataset

    • kaggle.com
    zip
    Updated Jun 17, 2023
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    sherief wafa (2023). Covid Dataset [Dataset]. https://www.kaggle.com/datasets/sheriefwafa/covid-dataset
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    zip(14254638 bytes)Available download formats
    Dataset updated
    Jun 17, 2023
    Authors
    sherief wafa
    License

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

    Description

    This Dataset is useful in showing some points and they are as follows.

    • Looking at Total_Cases VS Total_deaths
    • Shows liklihood of dying if you contract covid in your country
    • Looking at Total_cases vs Population
    • Shows what percentage of population got Covid
    • Looking at Countries with Highest infection rate compared to Population
    • Showing countries with highest death count per population
    • Showing continents with the Highest death count per population
    • Looking at Total population vs vaccination

    Here`s a link for the code used https://github.com/Sheriefwafa/portfolioproject_Covid/blob/main/COVID.sql

    and here`s also the Dashboard in Tableau https://public.tableau.com/app/profile/sherief.wafa/viz/CovidDashboard_16862572745460/Dashboard1

  3. Provisional COVID-19 death counts, rates, and percent of total deaths, by...

    • catalog.data.gov
    • data.virginia.gov
    • +2more
    Updated Sep 26, 2025
    + more versions
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    Centers for Disease Control and Prevention (2025). Provisional COVID-19 death counts, rates, and percent of total deaths, by jurisdiction of residence [Dataset]. https://catalog.data.gov/dataset/provisional-covid-19-death-counts-rates-and-percent-of-total-deaths-by-jurisdiction-of-res
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    Dataset updated
    Sep 26, 2025
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    Description

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

  4. d

    COVID-19 Outcomes by Vaccination Status - Historical

    • catalog.data.gov
    • data.cityofchicago.org
    • +2more
    Updated May 24, 2024
    + more versions
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    data.cityofchicago.org (2024). COVID-19 Outcomes by Vaccination Status - Historical [Dataset]. https://catalog.data.gov/dataset/covid-19-outcomes-by-vaccination-status
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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. Weekly rates of COVID-19 cases, hospitalizations, and deaths among people living in Chicago by vaccination status and age. Rates for fully vaccinated and unvaccinated begin the week ending April 3, 2021 when COVID-19 vaccines became widely available in Chicago. Rates for boosted begin the week ending October 23, 2021 after booster shots were recommended by the Centers for Disease Control and Prevention (CDC) for adults 65+ years old and adults in certain populations and high risk occupational and institutional settings who received Pfizer or Moderna for their primary series or anyone who received the Johnson & Johnson vaccine. Chicago residency is based on home address, as reported in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE) and Illinois National Electronic Disease Surveillance System (I-NEDSS). Outcomes: • Cases: People with a positive molecular (PCR) or antigen COVID-19 test result from an FDA-authorized COVID-19 test that was reported into I-NEDSS. A person can become re-infected with SARS-CoV-2 over time and so may be counted more than once in this dataset. Cases are counted by week the test specimen was collected. • Hospitalizations: COVID-19 cases who are hospitalized due to a documented COVID-19 related illness or who are admitted for any reason within 14 days of a positive SARS-CoV-2 test. Hospitalizations are counted by week of hospital admission. • Deaths: COVID-19 cases who died from COVID-19-related health complications as determined by vital records or a public health investigation. Deaths are counted by week of death. Vaccination status: • Fully vaccinated: Completion of primary series of a U.S. Food and Drug Administration (FDA)-authorized or approved COVID-19 vaccine at least 14 days prior to a positive test (with no other positive tests in the previous 45 days). • Boosted: Fully vaccinated with an additional or booster dose of any FDA-authorized or approved COVID-19 vaccine received at least 14 days prior to a positive test (with no other positive tests in the previous 45 days). • Unvaccinated: No evidence of having received a dose of an FDA-authorized or approved vaccine prior to a positive test. CLARIFYING NOTE: Those who started but did not complete all recommended doses of an FDA-authorized or approved vaccine prior to a positive test (i.e., partially vaccinated) are excluded from this dataset. Incidence rates for fully vaccinated but not boosted people (Vaccinated columns) are calculated as total fully vaccinated but not boosted with outcome divided by cumulative fully vaccinated but not boosted at the end of each week. Incidence rates for boosted (Boosted columns) are calculated as total boosted with outcome divided by cumulative boosted at the end of each week. Incidence rates for unvaccinated (Unvaccinated columns) are calculated as total unvaccinated with outcome divided by total population minus cumulative boosted, fully, and partially vaccinated at the end of each week. All rates are multiplied by 100,000. Incidence rate ratios (IRRs) are calculated by dividing the weekly incidence rates among unvaccinated people by those among fully vaccinated but not boosted and boosted people. Overall age-adjusted incidence rates and IRRs are standardized using the 2000 U.S. Census standard population. Population totals are from U.S. Census Bureau American Community Survey 1-year estimates for 2019. All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. This dataset reflects data known to CDPH at the time when the dataset is updated each week. Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined. For all datasets related to COVID-19, see https://data.cityofchic

  5. f

    Data_Sheet_1_The North American Layman's Understanding of COVID-19: Are We...

    • frontiersin.figshare.com
    • datasetcatalog.nlm.nih.gov
    pdf
    Updated Jun 2, 2023
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    Ali Salimi; Hassan ElHawary; Nermin Diab; Lee Smith (2023). Data_Sheet_1_The North American Layman's Understanding of COVID-19: Are We Doing Enough?.pdf [Dataset]. http://doi.org/10.3389/fpubh.2020.00358.s001
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    pdfAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    Frontiers
    Authors
    Ali Salimi; Hassan ElHawary; Nermin Diab; Lee Smith
    License

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

    Area covered
    North America
    Description

    Background: In the absence of an effective vaccine, public health policies are aimed at awareness, and education of the general public in order to contain the quickly spreading COVID-19 pandemic. Most of the recommended precautionary measures are dependent on human behaviors and therefore their effectiveness largely depends on peoples' perception and attitudes toward the disease. This study aimed to assess the level of knowledge, risk perception, and precautionary measures taken in response to COVID-19 in North America.Methods: In this cross-sectional observational study, an online survey targeted to North Americans focused on the public's knowledge of COVID-19, risk perception, and precautionary behaviors taken in response to this pandemic. Descriptive analyses were performed for the whole population and the subgroup analyses contrasted the differences between Americans and Canadians.Results: The cohort comprised 1,264 relatively young participants with an average age of 28.6 ± 9.8 years. The vast majority (>90%) were knowledgeable about COVID-19. Regarding risk perception, about a quarter assumed to be at less risk to contract the disease, and 42.8% considered themselves to be less contagious than others. While the vast majority avoided performing risky behaviors, only a small proportion (13.2%) wore a face mask—which is in line with the public health recommendations of the two countries at the time of data collection. Overall, a larger proportion of Canadian participants (55.8%) were satisfied with the performance of their national public health in response to the current pandemic, compared to their American counterparts (12.2%).Discussion: Data regarding the public's knowledge of COVID-19, risk perception, and behaviors in response to this pandemic is limited. The results of this study highlight that this relatively young and educated sample of North Americans had a high level of knowledge about COVID-19 and a large proportion of them were taking the precautionary measures against this pandemic. However, a significant number of individuals believe to be at less risk of contracting the disease compared to the general population. Educating the public that no one is safe from this disease, could play a role in further limiting risky behaviors and ultimately facilitating disease containment.

  6. Univariable analyses of predictive variables and root-transformed COVID-19...

    • plos.figshare.com
    xls
    Updated Jun 13, 2023
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    Taehee Chang; Bong-Kwang Jung; Jong-Yil Chai; Sung-il Cho (2023). Univariable analyses of predictive variables and root-transformed COVID-19 incidence rates. [Dataset]. http://doi.org/10.1371/journal.pntd.0010826.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 13, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Taehee Chang; Bong-Kwang Jung; Jong-Yil Chai; Sung-il Cho
    License

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

    Description

    Univariable analyses of predictive variables and root-transformed COVID-19 incidence rates.

  7. Summary of associations between parasitic disease variables and...

    • plos.figshare.com
    xls
    Updated Jun 13, 2023
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    Taehee Chang; Bong-Kwang Jung; Jong-Yil Chai; Sung-il Cho (2023). Summary of associations between parasitic disease variables and root-transformed COVID-19 incidence rates. [Dataset]. http://doi.org/10.1371/journal.pntd.0010826.t004
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 13, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Taehee Chang; Bong-Kwang Jung; Jong-Yil Chai; Sung-il Cho
    License

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

    Description

    Summary of associations between parasitic disease variables and root-transformed COVID-19 incidence rates.

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New York Times, Coronavirus (Covid-19) Data in the United States [Dataset]. https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html

Coronavirus (Covid-19) Data in the United States

Explore at:
Dataset provided by
New York Times
Description

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

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

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

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