17 datasets found
  1. f

    Correlates of COVID-19 case rates and mortality rates.

    • plos.figshare.com
    xls
    Updated Jun 12, 2023
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    Qian Huang; Sarah Jackson; Sahar Derakhshan; Logan Lee; Erika Pham; Amber Jackson; Susan L. Cutter (2023). Correlates of COVID-19 case rates and mortality rates. [Dataset]. http://doi.org/10.1371/journal.pone.0246548.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 12, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Qian Huang; Sarah Jackson; Sahar Derakhshan; Logan Lee; Erika Pham; Amber Jackson; Susan L. Cutter
    License

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

    Description

    Correlates of COVID-19 case rates and mortality rates.

  2. United States COVID-19 Community Levels by County

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

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

    Area covered
    United States
    Description

    Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

    This archived public use dataset has 11 data elements reflecting United States COVID-19 community levels for all available counties.

    The COVID-19 community levels were developed using a combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days. The COVID-19 community level was determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge.

    Using these data, the COVID-19 community level was classified as low, medium, or high.

    COVID-19 Community Levels were used to help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals.

    For the most accurate and up-to-date data for any county or state, visit the relevant health department website. COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates.

    Archived Data Notes:

    This dataset was renamed from "United States COVID-19 Community Levels by County as Originally Posted" to "United States COVID-19 Community Levels by County" on March 31, 2022.

    March 31, 2022: Column name for county population was changed to “county_population”. No change was made to the data points previous released.

    March 31, 2022: New column, “health_service_area_population”, was added to the dataset to denote the total population in the designated Health Service Area based on 2019 Census estimate.

    March 31, 2022: FIPS codes for territories American Samoa, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands were re-formatted to 5-digit numeric for records released on 3/3/2022 to be consistent with other records in the dataset.

    March 31, 2022: Changes were made to the text fields in variables “county”, “state”, and “health_service_area” so the formats are consistent across releases.

    March 31, 2022: The “%” sign was removed from the text field in column “covid_inpatient_bed_utilization”. No change was made to the data. As indicated in the column description, values in this column represent the percentage of staffed inpatient beds occupied by COVID-19 patients (7-day average).

    March 31, 2022: Data values for columns, “county_population”, “health_service_area_number”, and “health_service_area” were backfilled for records released on 2/24/2022. These columns were added since the week of 3/3/2022, thus the values were previously missing for records released the week prior.

    April 7, 2022: Updates made to data released on 3/24/2022 for Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands to correct a data mapping error.

    April 21, 2022: COVID-19 Community Level (CCL) data released for counties in Nebraska for the week of April 21, 2022 have 3 counties identified in the high category and 37 in the medium category. CDC has been working with state officials to verify the data submitted, as other data systems are not providing alerts for substantial increases in disease transmission or severity in the state.

    May 26, 2022: COVID-19 Community Level (CCL) data released for McCracken County, KY for the week of May 5, 2022 have been updated to correct a data processing error. McCracken County, KY should have appeared in the low community level category during the week of May 5, 2022. This correction is reflected in this update.

    May 26, 2022: COVID-19 Community Level (CCL) data released for several Florida counties for the week of May 19th, 2022, have been corrected for a data processing error. Of note, Broward, Miami-Dade, Palm Beach Counties should have appeared in the high CCL category, and Osceola County should have appeared in the medium CCL category. These corrections are reflected in this update.

    May 26, 2022: COVID-19 Community Level (CCL) data released for Orange County, New York for the week of May 26, 2022 displayed an erroneous case rate of zero and a CCL category of low due to a data source error. This county should have appeared in the medium CCL category.

    June 2, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a data processing error. Tolland County, CT should have appeared in the medium community level category during the week of May 26, 2022. This correction is reflected in this update.

    June 9, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a misspelling. The medium community level category for Tolland County, CT on the week of May 26, 2022 was misspelled as “meduim” in the data set. This correction is reflected in this update.

    June 9, 2022: COVID-19 Community Level (CCL) data released for Mississippi counties for the week of June 9, 2022 should be interpreted with caution due to a reporting cadence change over the Memorial Day holiday that resulted in artificially inflated case rates in the state.

    July 7, 2022: COVID-19 Community Level (CCL) data released for Rock County, Minnesota for the week of July 7, 2022 displayed an artificially low case rate and CCL category due to a data source error. This county should have appeared in the high CCL category.

    July 14, 2022: COVID-19 Community Level (CCL) data released for Massachusetts counties for the week of July 14, 2022 should be interpreted with caution due to a reporting cadence change that resulted in lower than expected case rates and CCL categories in the state.

    July 28, 2022: COVID-19 Community Level (CCL) data released for all Montana counties for the week of July 21, 2022 had case rates of 0 due to a reporting issue. The case rates have been corrected in this update.

    July 28, 2022: COVID-19 Community Level (CCL) data released for Alaska for all weeks prior to July 21, 2022 included non-resident cases. The case rates for the time series have been corrected in this update.

    July 28, 2022: A laboratory in Nevada reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate will be inflated in Clark County, NV for the week of July 28, 2022.

    August 4, 2022: COVID-19 Community Level (CCL) data was updated on August 2, 2022 in error during performance testing. Data for the week of July 28, 2022 was changed during this update due to additional case and hospital data as a result of late reporting between July 28, 2022 and August 2, 2022. Since the purpose of this data set is to provide point-in-time views of COVID-19 Community Levels on Thursdays, any changes made to the data set during the August 2, 2022 update have been reverted in this update.

    August 4, 2022: COVID-19 Community Level (CCL) data for the week of July 28, 2022 for 8 counties in Utah (Beaver County, Daggett County, Duchesne County, Garfield County, Iron County, Kane County, Uintah County, and Washington County) case data was missing due to data collection issues. CDC and its partners have resolved the issue and the correction is reflected in this update.

    August 4, 2022: Due to a reporting cadence change, case rates for all Alabama counties will be lower than expected. As a result, the CCL levels published on August 4, 2022 should be interpreted with caution.

    August 11, 2022: COVID-19 Community Level (CCL) data for the week of August 4, 2022 for South Carolina have been updated to correct a data collection error that resulted in incorrect case data. CDC and its partners have resolved the issue and the correction is reflected in this update.

    August 18, 2022: COVID-19 Community Level (CCL) data for the week of August 11, 2022 for Connecticut have been updated to correct a data ingestion error that inflated the CT case rates. CDC, in collaboration with CT, has resolved the issue and the correction is reflected in this update.

    August 25, 2022: A laboratory in Tennessee reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate may be inflated in many counties and the CCLs published on August 25, 2022 should be interpreted with caution.

    August 25, 2022: Due to a data source error, the 7-day case rate for St. Louis County, Missouri, is reported as zero in the COVID-19 Community Level data released on August 25, 2022. Therefore, the COVID-19 Community Level for this county should be interpreted with caution.

    September 1, 2022: Due to a reporting issue, case rates for all Nebraska counties will include 6 days of data instead of 7 days in the COVID-19 Community Level (CCL) data released on September 1, 2022. Therefore, the CCLs for all Nebraska counties should be interpreted with caution.

    September 8, 2022: Due to a data processing error, the case rate for Philadelphia County, Pennsylvania,

  3. NC COVID-19 Cases & Deaths

    • catalog.data.gov
    • data.townofcary.org
    • +1more
    Updated Oct 19, 2024
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    NC Department of Health and Human Services (2024). NC COVID-19 Cases & Deaths [Dataset]. https://catalog.data.gov/dataset/nc-covid-19-cases-deaths
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    Dataset updated
    Oct 19, 2024
    Dataset provided by
    North Carolina Department of Health and Human Serviceshttps://www.ncdhhs.gov/
    Area covered
    North Carolina
    Description

    This dataset contains counts of COVID-19 cases and deaths in North Carolina from March 2, 2020 to May 31, 2021. The data was extracted from NC Department of Health and Human Services' NC COVID-19 dashboard: Daily Cases and Deaths Metrics. This dataset is an archive - it is not being updated. Data Source: NCDHHS (2021). Daily Cases and Deaths Metrics (Version 1.3) [Data set]. https://covid19.ncdhhs.gov/dashboard/data-behind-dashboards

  4. n

    Coronavirus (Covid-19) Data in the United States

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

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

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

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

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

  5. Rate of U.S. COVID-19 cases as of March 10, 2023, by state

    • statista.com
    Updated May 15, 2024
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    Statista (2024). Rate of U.S. COVID-19 cases as of March 10, 2023, by state [Dataset]. https://www.statista.com/statistics/1109004/coronavirus-covid19-cases-rate-us-americans-by-state/
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    Dataset updated
    May 15, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    As of March 10, 2023, the state with the highest rate of COVID-19 cases was Rhode Island followed by Alaska. Around 103.9 million cases have been reported across the United States, with the states of California, Texas, and Florida reporting the highest numbers of infections.

    From an epidemic to a pandemic The World Health Organization declared the COVID-19 outbreak as a pandemic on March 11, 2020. The term pandemic refers to multiple outbreaks of an infectious illness threatening multiple parts of the world at the same time; when the transmission is this widespread, it can no longer be traced back to the country where it originated. The number of COVID-19 cases worldwide is roughly 683 million, and it has affected almost every country in the world.

    The symptoms and those who are most at risk Most people who contract the virus will suffer only mild symptoms, such as a cough, a cold, or a high temperature. However, in more severe cases, the infection can cause breathing difficulties and even pneumonia. Those at higher risk include older persons and people with pre-existing medical conditions, including diabetes, heart disease, and lung disease. Those aged 85 years and older have accounted for around 27 percent of all COVID deaths in the United States, although this age group makes up just two percent of the total population

  6. f

    Counties with the highest ranking of confirmed case rates and mortality...

    • figshare.com
    xls
    Updated Jun 12, 2023
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    Qian Huang; Sarah Jackson; Sahar Derakhshan; Logan Lee; Erika Pham; Amber Jackson; Susan L. Cutter (2023). Counties with the highest ranking of confirmed case rates and mortality rates, March 1-September 5, 2020. [Dataset]. http://doi.org/10.1371/journal.pone.0246548.t002
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    xlsAvailable download formats
    Dataset updated
    Jun 12, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Qian Huang; Sarah Jackson; Sahar Derakhshan; Logan Lee; Erika Pham; Amber Jackson; Susan L. Cutter
    License

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

    Description

    Counties with the highest ranking of confirmed case rates and mortality rates, March 1-September 5, 2020.

  7. Rates of COVID-19 Cases or Deaths by Age Group and Vaccination Status and...

    • data.cdc.gov
    • data.virginia.gov
    • +1more
    application/rdfxml +5
    Updated Feb 22, 2023
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    CDC COVID-19 Response, Epidemiology Task Force (2023). Rates of COVID-19 Cases or Deaths by Age Group and Vaccination Status and Booster Dose [Dataset]. https://data.cdc.gov/Public-Health-Surveillance/Rates-of-COVID-19-Cases-or-Deaths-by-Age-Group-and/d6p8-wqjm
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    xml, csv, application/rssxml, application/rdfxml, json, tsvAvailable download formats
    Dataset updated
    Feb 22, 2023
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    Authors
    CDC COVID-19 Response, Epidemiology Task Force
    Description

    Data for CDC’s COVID Data Tracker site on Rates of COVID-19 Cases and Deaths by Vaccination Status. Click 'More' for important dataset description and footnotes

    Dataset and data visualization details: These data were posted on October 21, 2022, archived on November 18, 2022, and revised on February 22, 2023. These data reflect cases among persons with a positive specimen collection date through September 24, 2022, and deaths among persons with a positive specimen collection date through September 3, 2022.

    Vaccination status: A person vaccinated with a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing the primary series of an FDA-authorized or approved COVID-19 vaccine. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. Additional or booster dose: A person vaccinated with a primary series and an additional or booster dose had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after receipt of an additional or booster dose of any COVID-19 vaccine on or after August 13, 2021. For people ages 18 years and older, data are graphed starting the week including September 24, 2021, when a COVID-19 booster dose was first recommended by CDC for adults 65+ years old and people in certain populations and high risk occupational and institutional settings. For people ages 12-17 years, data are graphed starting the week of December 26, 2021, 2 weeks after the first recommendation for a booster dose for adolescents ages 16-17 years. For people ages 5-11 years, data are included starting the week of June 5, 2022, 2 weeks after the first recommendation for a booster dose for children aged 5-11 years. For people ages 50 years and older, data on second booster doses are graphed starting the week including March 29, 2022, when the recommendation was made for second boosters. Vertical lines represent dates when changes occurred in U.S. policy for COVID-19 vaccination (details provided above). Reporting is by primary series vaccine type rather than additional or booster dose vaccine type. The booster dose vaccine type may be different than the primary series vaccine type. ** Because data on the immune status of cases and associated deaths are unavailable, an additional dose in an immunocompromised person cannot be distinguished from a booster dose. This is a relevant consideration because vaccines can be less effective in this group. Deaths: A COVID-19–associated death occurred in a person with a documented COVID-19 diagnosis who died; health department staff reviewed to make a determination using vital records, public health investigation, or other data sources. Rates of COVID-19 deaths by vaccination status are reported based on when the patient was tested for COVID-19, not the date they died. Deaths usually occur up to 30 days after COVID-19 diagnosis. Participating jurisdictions: Currently, these 31 health departments that regularly link their case surveillance to immunization information system data are included in these incidence rate estimates: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, District of Columbia, Florida, Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Michigan, Minnesota, Nebraska, New Jersey, New Mexico, New York, New York City (New York), North Carolina, Philadelphia (Pennsylvania), Rhode Island, South Dakota, Tennessee, Texas, Utah, Washington, and West Virginia; 30 jurisdictions also report deaths among vaccinated and unvaccinated people. These jurisdictions represent 72% of the total U.S. population and all ten of the Health and Human Services Regions. Data on cases among people who received additional or booster doses were reported from 31 jurisdictions; 30 jurisdictions also reported data on deaths among people who received one or more additional or booster dose; 28 jurisdictions reported cases among people who received two or more additional or booster doses; and 26 jurisdictions reported deaths among people who received two or more additional or booster doses. This list will be updated as more jurisdictions participate. Incidence rate estimates: Weekly age-specific incidence rates by vaccination status were calculated as the number of cases or deaths divided by the number of people vaccinated with a primary series, overall or with/without a booster dose (cumulative) or unvaccinated (obtained by subtracting the cumulative number of people vaccinated with a primary series and partially vaccinated people from the 2019 U.S. intercensal population estimates) and multiplied by 100,000. Overall incidence rates were age-standardized using the 2000 U.S. Census standard population. To estimate population counts for ages 6 months through 1 year, half of the single-year population counts for ages 0 through 1 year were used. All rates are plotted by positive specimen collection date to reflect when incident infections occurred. For the primary series analysis, age-standardized rates include ages 12 years and older from April 4, 2021 through December 4, 2021, ages 5 years and older from December 5, 2021 through July 30, 2022 and ages 6 months and older from July 31, 2022 onwards. For the booster dose analysis, age-standardized rates include ages 18 years and older from September 19, 2021 through December 25, 2021, ages 12 years and older from December 26, 2021, and ages 5 years and older from June 5, 2022 onwards. Small numbers could contribute to less precision when calculating death rates among some groups. Continuity correction: A continuity correction has been applied to the denominators by capping the percent population coverage at 95%. To do this, we assumed that at least 5% of each age group would always be unvaccinated in each jurisdiction. Adding this correction ensures that there is always a reasonable denominator for the unvaccinated population that would prevent incidence and death rates from growing unrealistically large due to potential overestimates of vaccination coverage. Incidence rate ratios (IRRs): IRRs for the past one month were calculated by dividing the average weekly incidence rates among unvaccinated people by that among people vaccinated with a primary series either overall or with a booster dose. Publications: Scobie HM, Johnson AG, Suthar AB, et al. Monitoring Incidence of COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Status — 13 U.S. Jurisdictions, April 4–July 17, 2021. MMWR Morb Mortal Wkly Rep 2021;70:1284–1290. Johnson AG, Amin AB, Ali AR, et al. COVID-19 Incidence and Death Rates Among Unvaccinated and Fully Vaccinated Adults with and Without Booster Doses During Periods of Delta and Omicron Variant Emergence — 25 U.S. Jurisdictions, April 4–December 25, 2021. MMWR Morb Mortal Wkly Rep 2022;71:132–138

  8. f

    Goodness of fit results for smoothed 3-day average data.

    • plos.figshare.com
    xls
    Updated Jun 11, 2023
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    Andrew B. Lawson; Joanne Kim (2023). Goodness of fit results for smoothed 3-day average data. [Dataset]. http://doi.org/10.1371/journal.pone.0242777.t003
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    xlsAvailable download formats
    Dataset updated
    Jun 11, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Andrew B. Lawson; Joanne Kim
    License

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

    Description

    Goodness of fit results for smoothed 3-day average data.

  9. Number of SARS-CoV-2 variant B.1.351 cases in the U.S. as of Apr. 10, 2021,...

    • statista.com
    Updated Apr 27, 2021
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    Statista (2021). Number of SARS-CoV-2 variant B.1.351 cases in the U.S. as of Apr. 10, 2021, by state [Dataset]. https://www.statista.com/statistics/1207357/covid-19-variant-b1351-cases-number-us-by-state/
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    Dataset updated
    Apr 27, 2021
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    As of April 10, 2021, there had been 453 reported cases of the B.1.351 SARS-CoV-2 variant, with the highest number of cases found in the state of South Carolina. SARS-CoV-2 variants act differently than the original disease and therefore may spread more quickly or cause more severe disease. The rise of SARS-CoV-2 variants has become increasingly worrisome in many countries. This statistic shows the number of reported cases of SARS-CoV-2 variant B.1.351 - a variant first discovered in South Africa in late December 2020 - in the United States as of April 10, 2021, by state or territory.

  10. Provisional COVID-19 death counts and rates by month, jurisdiction of...

    • catalog.data.gov
    • data.virginia.gov
    • +3more
    Updated Jul 11, 2025
    + more versions
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    Centers for Disease Control and Prevention (2025). Provisional COVID-19 death counts and rates by month, jurisdiction of residence, and demographic characteristics [Dataset]. https://catalog.data.gov/dataset/provisional-covid-19-death-counts-and-rates-by-month-jurisdiction-of-residence-and-demogra
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    Dataset updated
    Jul 11, 2025
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    Description

    This file contains COVID-19 death counts and rates by month and year of death, jurisdiction of residence (U.S., HHS Region) and demographic characteristics (sex, age, race and Hispanic origin, and age/race and Hispanic origin). United States death counts and rates include the 50 states, plus the District of Columbia. 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 jurisdictions. 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; 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). Rate are based on deaths occurring in the specified week 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 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) 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.).

  11. f

    Model goodness of fit results: January 22nd to April 12th.

    • plos.figshare.com
    xls
    Updated Jun 11, 2023
    + more versions
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    Andrew B. Lawson; Joanne Kim (2023). Model goodness of fit results: January 22nd to April 12th. [Dataset]. http://doi.org/10.1371/journal.pone.0242777.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 11, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Andrew B. Lawson; Joanne Kim
    License

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

    Description

    Model goodness of fit results: January 22nd to April 12th.

  12. Total number of U.S. COVID-19 cases as of March 10, 2023, by state

    • statista.com
    Updated May 15, 2024
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    Statista (2024). Total number of U.S. COVID-19 cases as of March 10, 2023, by state [Dataset]. https://www.statista.com/statistics/1102807/coronavirus-covid19-cases-number-us-americans-by-state/
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    Dataset updated
    May 15, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    As of March 10, 2023, the state with the highest number of COVID-19 cases was California. Almost 104 million cases have been reported across the United States, with the states of California, Texas, and Florida reporting the highest numbers.

    From an epidemic to a pandemic The World Health Organization declared the COVID-19 outbreak a pandemic on March 11, 2020. The term pandemic refers to multiple outbreaks of an infectious illness threatening multiple parts of the world at the same time. When the transmission is this widespread, it can no longer be traced back to the country where it originated. The number of COVID-19 cases worldwide has now reached over 669 million.

    The symptoms and those who are most at risk Most people who contract the virus will suffer only mild symptoms, such as a cough, a cold, or a high temperature. However, in more severe cases, the infection can cause breathing difficulties and even pneumonia. Those at higher risk include older persons and people with pre-existing medical conditions, including diabetes, heart disease, and lung disease. People aged 85 years and older have accounted for around 27 percent of all COVID-19 deaths in the United States, although this age group makes up just two percent of the U.S. population

  13. f

    Data_Sheet_4_Implementation of a Rural Community Diagnostic Testing Strategy...

    • figshare.com
    xlsx
    Updated Jun 4, 2023
    + more versions
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    Emily V. Plumb; Rachel E. Ham; Justin M. Napolitano; Kylie L. King; Theodore J. Swann; Corey A. Kalbaugh; Lior Rennert; Delphine Dean (2023). Data_Sheet_4_Implementation of a Rural Community Diagnostic Testing Strategy for SARS-CoV-2 in Upstate South Carolina.xlsx [Dataset]. http://doi.org/10.3389/fpubh.2022.858421.s004
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    Frontiers
    Authors
    Emily V. Plumb; Rachel E. Ham; Justin M. Napolitano; Kylie L. King; Theodore J. Swann; Corey A. Kalbaugh; Lior Rennert; Delphine Dean
    License

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

    Area covered
    South Carolina, Upstate South Carolina
    Description

    By developing a partnership amongst a public university lab, local city government officials and community healthcare providers, we established a drive-through COVID-19 testing site aiming to improve access to SARS-CoV-2 testing in rural Upstate South Carolina. We collected information on symptoms and known exposures of individuals seeking testing to determine the number of pre- or asymptomatic individuals. We completed 71,102 SARS-CoV-2 tests in the community between December 2020-December 2021 and reported 91.49% of results within 24 h. We successfully identified 5,244 positive tests; 73.36% of these tests originated from individuals who did not report symptoms. Finally, we identified high transmission levels during two major surges and compared test positivity rates of the local and regional communities. Importantly, the local community had significantly lower test positivity rates than the regional community throughout 2021 (p < 0.001). While both communities reached peak case load and test positivity near the same time, the local community returned to moderate transmission as indicated by positivity 4 weeks before the regional community. Our university lab facilitated easy testing with fast turnaround times, which encouraged voluntary testing and helped identify a large number of non-symptomatic cases. Finding the balance of simplicity, accessibility, and community trust was vital to the success of our widespread community testing program for SARS-CoV-2.

  14. d

    Data from: Evolution and epidemic spread of SARS-CoV-2 in Brazil

    • datadryad.org
    • eprints.soton.ac.uk
    zip
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    Darlan S. Candido; Ingra M. Claro; Jaqueline G. de Jesus; William M. Souza; Filipe R. R. Moreira; Simon Dellicour; Thomas A. Mellan; Louis du Plessis; Rafael H. M. Pereira; Flavia C. S. Sales; Erika R. Manuli; Julien Thézé; Luiz Almeida; Mariane T. Menezes; Carolina M. Voloch; Marcilio J. Fumagalli; Thaís M. Coletti; Camila A. M. da Silva; Mariana S. Ramundo; Mariene R. Amorim; Henrique H. Hoeltgebaum; Swapnil Mishra; Mandev S. Gill; Luiz M. Carvalho; Lewis F. Buss; Carlos A. Prete; Jordan Ashworth; Helder I. Nakaya; Pedro S. Peixoto; Oliver J. Brady; Samuel M. Nicholls; Amilcar Tanuri; Átila D. Rossi; Carlos K.V. Braga; Alexandra L. Gerber; Ana Paula de C. Guimarães; Nelson Gaburo; Cecila Salete Alencar; Alessandro C.S. Ferreira; Cristiano X. Lima; José Eduardo Levi; Celso Granato; Giulia M. Ferreira; Ronaldo S. Francisco; Fabiana Granja; Marcia T. Garcia; Maria Luiza Moretti; Amilcar Tanuri; Mauricio W. Perroud; Átila D. Rossi; Carlos K.V. Braga; Alexandra L. Gerber; Terezinha M. P. P. Castiñeiras; Ana Paula de C. Guimarães; Nelson Gaburo; Carolina S. Lazari; Cecila Salete Alencar; Alessandro C.S. Ferreira; Cristiano X. Lima; Sarah C. Hill; José Eduardo Levi; Andreza Aruska de Souza Santos; Celso Granato; Giulia M. Ferreira; Ronaldo S. Francisco; Camila L. Simeoni; Fabiana Granja; Julia Forato; Marcia T. Garcia; Andrei C. Sposito; Maria Luiza Moretti; Mauricio W. Perroud; Angelica Z. Schreiber; Terezinha M. P. P. Castiñeiras; Carolina S. Lazari; Sarah C. Hill; Magnun N. N. Santos; Andreza Aruska de Souza Santos; Camila Zolini de Sá; Camila L. Simeoni; Julia Forato; Andrei C. Sposito; Renan P. Souza; Angelica Z. Schreiber; Luciana C. Resende-Moreira; Magnun N. N. Santos; Camila Zolini de Sá; Renan P. Souza; Luciana C. Resende-Moreira; Mauro M. Teixeira; Josy Hubner; Patricia A. F. Leme; Rennan G Moreira; Maurício L. Nogueira; Neil M Ferguson; Silvia F. Costa; José Luiz Proenca-Modena; Ana Tereza R. Vasconcelos; Samir Bhatt; Philippe Lemey; Chieh-Hsi Wu; Andrew Rambaut; Nick J. Loman; Renato S. Aguiar; Oliver G. Pybus; Ester C. Sabino; Nuno R. Faria, Evolution and epidemic spread of SARS-CoV-2 in Brazil [Dataset]. http://doi.org/10.5061/dryad.rxwdbrv5z
    Explore at:
    zipAvailable download formats
    Dataset provided by
    Dryad
    Authors
    Darlan S. Candido; Ingra M. Claro; Jaqueline G. de Jesus; William M. Souza; Filipe R. R. Moreira; Simon Dellicour; Thomas A. Mellan; Louis du Plessis; Rafael H. M. Pereira; Flavia C. S. Sales; Erika R. Manuli; Julien Thézé; Luiz Almeida; Mariane T. Menezes; Carolina M. Voloch; Marcilio J. Fumagalli; Thaís M. Coletti; Camila A. M. da Silva; Mariana S. Ramundo; Mariene R. Amorim; Henrique H. Hoeltgebaum; Swapnil Mishra; Mandev S. Gill; Luiz M. Carvalho; Lewis F. Buss; Carlos A. Prete; Jordan Ashworth; Helder I. Nakaya; Pedro S. Peixoto; Oliver J. Brady; Samuel M. Nicholls; Amilcar Tanuri; Átila D. Rossi; Carlos K.V. Braga; Alexandra L. Gerber; Ana Paula de C. Guimarães; Nelson Gaburo; Cecila Salete Alencar; Alessandro C.S. Ferreira; Cristiano X. Lima; José Eduardo Levi; Celso Granato; Giulia M. Ferreira; Ronaldo S. Francisco; Fabiana Granja; Marcia T. Garcia; Maria Luiza Moretti; Amilcar Tanuri; Mauricio W. Perroud; Átila D. Rossi; Carlos K.V. Braga; Alexandra L. Gerber; Terezinha M. P. P. Castiñeiras; Ana Paula de C. Guimarães; Nelson Gaburo; Carolina S. Lazari; Cecila Salete Alencar; Alessandro C.S. Ferreira; Cristiano X. Lima; Sarah C. Hill; José Eduardo Levi; Andreza Aruska de Souza Santos; Celso Granato; Giulia M. Ferreira; Ronaldo S. Francisco; Camila L. Simeoni; Fabiana Granja; Julia Forato; Marcia T. Garcia; Andrei C. Sposito; Maria Luiza Moretti; Mauricio W. Perroud; Angelica Z. Schreiber; Terezinha M. P. P. Castiñeiras; Carolina S. Lazari; Sarah C. Hill; Magnun N. N. Santos; Andreza Aruska de Souza Santos; Camila Zolini de Sá; Camila L. Simeoni; Julia Forato; Andrei C. Sposito; Renan P. Souza; Angelica Z. Schreiber; Luciana C. Resende-Moreira; Magnun N. N. Santos; Camila Zolini de Sá; Renan P. Souza; Luciana C. Resende-Moreira; Mauro M. Teixeira; Josy Hubner; Patricia A. F. Leme; Rennan G Moreira; Maurício L. Nogueira; Neil M Ferguson; Silvia F. Costa; José Luiz Proenca-Modena; Ana Tereza R. Vasconcelos; Samir Bhatt; Philippe Lemey; Chieh-Hsi Wu; Andrew Rambaut; Nick J. Loman; Renato S. Aguiar; Oliver G. Pybus; Ester C. Sabino; Nuno R. Faria
    Time period covered
    2020
    Area covered
    Brazil
    Description

    Please see Materials and Methods section in Supplementary Materials.

  15. f

    Data_Sheet_3_Implementation of a Rural Community Diagnostic Testing Strategy...

    • frontiersin.figshare.com
    xlsx
    Updated Jun 5, 2023
    + more versions
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    Emily V. Plumb; Rachel E. Ham; Justin M. Napolitano; Kylie L. King; Theodore J. Swann; Corey A. Kalbaugh; Lior Rennert; Delphine Dean (2023). Data_Sheet_3_Implementation of a Rural Community Diagnostic Testing Strategy for SARS-CoV-2 in Upstate South Carolina.xlsx [Dataset]. http://doi.org/10.3389/fpubh.2022.858421.s003
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Jun 5, 2023
    Dataset provided by
    Frontiers
    Authors
    Emily V. Plumb; Rachel E. Ham; Justin M. Napolitano; Kylie L. King; Theodore J. Swann; Corey A. Kalbaugh; Lior Rennert; Delphine Dean
    License

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

    Area covered
    South Carolina, Upstate South Carolina
    Description

    By developing a partnership amongst a public university lab, local city government officials and community healthcare providers, we established a drive-through COVID-19 testing site aiming to improve access to SARS-CoV-2 testing in rural Upstate South Carolina. We collected information on symptoms and known exposures of individuals seeking testing to determine the number of pre- or asymptomatic individuals. We completed 71,102 SARS-CoV-2 tests in the community between December 2020-December 2021 and reported 91.49% of results within 24 h. We successfully identified 5,244 positive tests; 73.36% of these tests originated from individuals who did not report symptoms. Finally, we identified high transmission levels during two major surges and compared test positivity rates of the local and regional communities. Importantly, the local community had significantly lower test positivity rates than the regional community throughout 2021 (p < 0.001). While both communities reached peak case load and test positivity near the same time, the local community returned to moderate transmission as indicated by positivity 4 weeks before the regional community. Our university lab facilitated easy testing with fast turnaround times, which encouraged voluntary testing and helped identify a large number of non-symptomatic cases. Finding the balance of simplicity, accessibility, and community trust was vital to the success of our widespread community testing program for SARS-CoV-2.

  16. f

    Table_1_Assessing the Impact of Neighborhood Socioeconomic Characteristics...

    • frontiersin.figshare.com
    docx
    Updated Jun 1, 2023
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    Elham Hatef; Hsien-Yen Chang; Christopher Kitchen; Jonathan P. Weiner; Hadi Kharrazi (2023). Table_1_Assessing the Impact of Neighborhood Socioeconomic Characteristics on COVID-19 Prevalence Across Seven States in the United States.DOCX [Dataset]. http://doi.org/10.3389/fpubh.2020.571808.s001
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    docxAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    Frontiers
    Authors
    Elham Hatef; Hsien-Yen Chang; Christopher Kitchen; Jonathan P. Weiner; Hadi Kharrazi
    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

    Introduction: The spread of Coronavirus Disease 2019 (COVID-19) across the United States has highlighted the long-standing nationwide health inequalities with socioeconomically challenged communities experiencing a higher burden of the disease. We assessed the impact of neighborhood socioeconomic characteristics on the COVID-19 prevalence across seven selected states (i.e., Arizona, Florida, Illinois, Maryland, North Carolina, South Carolina, and Virginia).Methods: We obtained cumulative COVID-19 cases reported at the neighborhood aggregation level by Departments of Health in selected states on two dates (May 3rd, 2020, and May 30th, 2020) and assessed the correlation between the COVID-19 prevalence and neighborhood characteristics. We developed Area Deprivation Index (ADI), a composite measure to rank neighborhoods by their socioeconomic characteristics, using the 2018 US Census American Community Survey. The higher ADI rank represented more disadvantaged neighborhoods.Results: After controlling for age, gender, and the square mileage of each community we identified Zip-codes with higher ADI (more disadvantaged neighborhoods) in Illinois and Maryland had higher COVID-19 prevalence comparing to zip-codes across the country and in the same state with lower ADI (less disadvantaged neighborhoods) using data on May 3rd. We detected the same pattern across all states except for Florida and Virginia using data on May 30th, 2020.Conclusion: Our study provides evidence that not all Americans are at equal risk for COVID-19. Socioeconomic characteristics of communities appear to be associated with their COVID-19 susceptibility, at least among those study states with high rates of disease.

  17. Characteristics of adults who received care for COVID-like illness at a SC...

    • plos.figshare.com
    xls
    Updated Mar 7, 2024
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    Theodoros V. Giannouchos; Nicole L. Hair; Bankole Olatosi; Xiaoming Li (2024). Characteristics of adults who received care for COVID-like illness at a SC hospital or emergency department by SARS-CoV-2 test result. [Dataset]. http://doi.org/10.1371/journal.pone.0300198.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Mar 7, 2024
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Theodoros V. Giannouchos; Nicole L. Hair; Bankole Olatosi; Xiaoming Li
    License

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

    Description

    Characteristics of adults who received care for COVID-like illness at a SC hospital or emergency department by SARS-CoV-2 test result.

  18. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Qian Huang; Sarah Jackson; Sahar Derakhshan; Logan Lee; Erika Pham; Amber Jackson; Susan L. Cutter (2023). Correlates of COVID-19 case rates and mortality rates. [Dataset]. http://doi.org/10.1371/journal.pone.0246548.t003

Correlates of COVID-19 case rates and mortality rates.

Related Article
Explore at:
xlsAvailable download formats
Dataset updated
Jun 12, 2023
Dataset provided by
PLOS ONE
Authors
Qian Huang; Sarah Jackson; Sahar Derakhshan; Logan Lee; Erika Pham; Amber Jackson; Susan L. Cutter
License

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

Description

Correlates of COVID-19 case rates and mortality rates.

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