100+ datasets found
  1. n

    Coronavirus (Covid-19) Data in the United States

    • nytimes.com
    • openicpsr.org
    • +2more
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    New York Times, Coronavirus (Covid-19) Data in the United States [Dataset]. https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html
    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.

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

    • statista.com
    Updated May 15, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    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/
    Explore at:
    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

  3. COVID-19 death rates in the United States as of March 10, 2023, by state

    • statista.com
    Updated May 15, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2024). COVID-19 death rates in the United States as of March 10, 2023, by state [Dataset]. https://www.statista.com/statistics/1109011/coronavirus-covid19-death-rates-us-by-state/
    Explore at:
    Dataset updated
    May 15, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    As of March 10, 2023, the death rate from COVID-19 in the state of New York was 397 per 100,000 people. New York is one of the states with the highest number of COVID-19 cases.

  4. c

    The COVID Tracking Project

    • covidtracking.com
    google sheets
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    The COVID Tracking Project [Dataset]. https://covidtracking.com/
    Explore at:
    google sheetsAvailable download formats
    Description

    The COVID Tracking Project collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data we can collect for the novel coronavirus, SARS-CoV-2. We attempt to include positive and negative results, pending tests, and total people tested for each state or district currently reporting that data.

    Testing is a crucial part of any public health response, and sharing test data is essential to understanding this outbreak. The CDC is currently not publishing complete testing data, so we’re doing our best to collect it from each state and provide it to the public. The information is patchy and inconsistent, so we’re being transparent about what we find and how we handle it—the spreadsheet includes our live comments about changing data and how we’re working with incomplete information.

    From here, you can also learn about our methodology, see who makes this, and find out what information states provide and how we handle it.

  5. g

    Coronavirus COVID-19 Global Cases by the Center for Systems Science and...

    • github.com
    • systems.jhu.edu
    • +1more
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE), Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU) [Dataset]. https://github.com/CSSEGISandData/COVID-19
    Explore at:
    Dataset provided by
    Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE)
    Area covered
    Global
    Description

    2019 Novel Coronavirus COVID-19 (2019-nCoV) Visual Dashboard and Map:
    https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

    • Confirmed Cases by Country/Region/Sovereignty
    • Confirmed Cases by Province/State/Dependency
    • Deaths
    • Recovered

    Downloadable data:
    https://github.com/CSSEGISandData/COVID-19

    Additional Information about the Visual Dashboard:
    https://systems.jhu.edu/research/public-health/ncov

  6. COVID-19 Trends in Each Country

    • data.amerigeoss.org
    esri rest, html
    Updated Jul 29, 2020
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    ESRI (2020). COVID-19 Trends in Each Country [Dataset]. https://data.amerigeoss.org/it/dataset/covid-19-trends-in-each-country
    Explore at:
    html, esri restAvailable download formats
    Dataset updated
    Jul 29, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Description

    COVID-19 Trends Methodology
    Our goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.


    6/24/2020 - Expanded Case Rates discussion to include fix on 6/23 for calculating active cases.
    6/22/2020 - Added Executive Summary and Subsequent Outbreaks sections
    Revisions on 6/10/2020 based on updated CDC reporting. This affects the estimate of active cases by revising the average duration of cases with hospital stays downward from 30 days to 25 days. The result shifted 76 U.S. counties out of Epidemic to Spreading trend and no change for national level trends.
    Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.
    Correction on 6/1/2020
    Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020.
    Revisions added on 4/30/2020 are highlighted.
    Revisions added on 4/23/2020 are highlighted.

    Executive Summary
    COVID-19 Trends is a methodology for characterizing the current trend for places during the COVID-19 global pandemic. Each day we assign one of five trends: Emergent, Spreading, Epidemic, Controlled, or End Stage to geographic areas to geographic areas based on the number of new cases, the number of active cases, the total population, and an algorithm (described below) that contextualize the most recent fourteen days with the overall COVID-19 case history. Currently we analyze the countries of the world and the U.S. Counties.
    The purpose is to give policymakers, citizens, and analysts a fact-based data driven sense for the direction each place is currently going. When a place has the initial cases, they are assigned Emergent, and if that place controls the rate of new cases, they can move directly to Controlled, and even to End Stage in a short time. However, if the reporting or measures to curtail spread are not adequate and significant numbers of new cases continue, they are assigned to Spreading, and in cases where the spread is clearly uncontrolled, Epidemic trend.

    We analyze the data reported by Johns Hopkins University to produce the trends, and we report the rates of cases, spikes of new cases, the number of days since the last reported case, and number of deaths. We also make adjustments to the assignments based on population so rural areas are not assigned trends based solely on case rates, which can be quite high relative to local populations.

    Two key factors are not consistently known or available and should be taken into consideration with the assigned trend. First is the amount of resources, e.g., hospital beds, physicians, etc.that are currently available in each area. Second is the number of recoveries, which are often not tested or reported. On the latter, we provide a probable number of active cases based on CDC guidance for the typical duration of mild to severe cases.

    Reasons for undertaking this work in March of 2020:
    1. The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.
    2. The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.
    3. The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:
    • U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online.
    • Initial older guidance was also obtained online.
    Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws.
    Thus, the formula used to compute an estimate of active cases is:

    Active Cases = 100% of new cases in past 14 days + 19% from past 15-25 days + 5% from past 26-49 days - total deaths.
    <br

  7. United States COVID-19 County Level Data Sources - ARCHIVED

    • odgavaprod.ogopendata.com
    • healthdata.gov
    • +1more
    csv, json, rdf, xsl
    Updated Feb 23, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Centers for Disease Control and Prevention (2025). United States COVID-19 County Level Data Sources - ARCHIVED [Dataset]. https://odgavaprod.ogopendata.com/dataset/united-states-covid-19-county-level-data-sources-archived
    Explore at:
    json, csv, rdf, xslAvailable download formats
    Dataset updated
    Feb 23, 2025
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    Area covered
    United States
    Description

    The Public Health Emergency (PHE) declaration for COVID-19 expired on May 11, 2023. As a result, the Aggregate Case and Death Surveillance System will be discontinued. Although these data will continue to be publicly available, this dataset will no longer be updated.

    On October 20, 2022, CDC began retrieving aggregate case and death data from jurisdictional and state partners weekly instead of daily.

    This dataset includes the URLs that were used by the aggregate county data collection process that compiled aggregate case and death counts by county. Within this file, each of the states (plus select jurisdictions and territories) are listed along with the county web sources which were used for pulling these numbers. Some states had a single statewide source for collecting the county data, while other states and local health jurisdictions may have had standalone sources for individual counties. In the cases where both local and state web sources were listed, a composite approach was taken so that the maximum value reported for a location from either source was used. The initial raw data were sourced from these links and ingested into the CDC aggregate county dataset before being published on the COVID Data Tracker.

  8. m

    COVID-19 NE Dataset

    • data.mendeley.com
    • narcis.nl
    Updated Aug 18, 2020
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Joel Mintz (2020). COVID-19 NE Dataset [Dataset]. http://doi.org/10.17632/42wzh29xrp.2
    Explore at:
    Dataset updated
    Aug 18, 2020
    Authors
    Joel Mintz
    License

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

    Description

    COVID-19 Dataset for Correlation Between Early Government Interventions in the Northeastern United States and Peak COVID-19 Disease Burden by Joel Mintz. File Type: Excel Contents: Tab 1 ("Raw")=Raw Data as Downloaded directly from COVID Tracking Project, sorted by date Tab 2-14 ("State Name') = Data Sorted by State Tab 2-14 Headers: Column 1: Population per state, as recorded by latest American Community Survey, maximum (peak) COVID-19 outcome, with date on which outcome occurred. Column 2: Date on which numbers were recorded* Column 3: State Name* Column 4: Number of reported positive COVID-19 tests* Column 5: Number of reported negative COVID-19 tests* Column 6: Pending COVID-19 tests* Column 7: Currently Hospitalized* Column 8: Cumulatively Hospitalized* Column 9: Currently in ICU* Column 10: Cumulatively in ICU* Column 11: Currently on Ventilator Support* Column 12: Cumulatively on Ventilator Support* Column 13: Total Recovered* Column 14: Cumulative Mortality* *Provided in Original Raw Data Column 15: Total Tests Administered (Column 4+Column 5) Column 16: Placeholder Column 17: % of total population tested Column 18: New Cases Per day Column 19: Change in new cases per day Column 20: Positive cases per day per capita in number per/ hundreds of thousands: (Column 18/total population*100000) Column 21: Change in Positive cases per day per capita in number per/ hundreds of thousands: (Column 19/total population*100000) Column 22: Hospitalizations per day per capita in number per/ hundreds of thousands Column 23: Change in Hospitalizations per day per capita in number per/ hundreds of thousands Column 24: Deaths per day per capita in number per/ hundreds of thousands Column 25: Change in Deaths per day per capita in number per/ hundreds of thousands Column 26-31: Columns 20-25 with an applied 5 day moving average filter Column 32: Adjusted hospitalization: (Subtract number of hospitalizations from the initial number of hospitalzations where reporting bean) Column 33: Adjusted hospitalizations per day per capita Column 34: Adjusted hospitalizations per day per capita, with applied 5 day moving average filter

  9. f

    Average number of daily new infections in the simulation for the first 226...

    • figshare.com
    xlsx
    Updated Jun 4, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Pinar Keskinocak; Buse Eylul Oruc; Arden Baxter; John Asplund; Nicoleta Serban (2023). Average number of daily new infections in the simulation for the first 226 days in counties. [Dataset]. http://doi.org/10.1371/journal.pone.0239798.s021
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Pinar Keskinocak; Buse Eylul Oruc; Arden Baxter; John Asplund; Nicoleta Serban
    License

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

    Description

    Data values are averages from the 30 runs. Since the simulation is based on the population of Georgia, and each entity in the simulation represents ten people, all data values recorded are based on one-tenth of the population of Georgia, that is, there are a total of roughly one million people in the simulation. (XLSX)

  10. d

    Data from: COVID-19 prevalence and predictors in United States adults during...

    • search.dataone.org
    • data.niaid.nih.gov
    • +1more
    Updated Apr 30, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Robert Morlock (2025). COVID-19 prevalence and predictors in United States adults during peak stay-at-home orders [Dataset]. http://doi.org/10.5061/dryad.2547d7wpq
    Explore at:
    Dataset updated
    Apr 30, 2025
    Dataset provided by
    Dryad Digital Repository
    Authors
    Robert Morlock
    Time period covered
    Jan 1, 2021
    Area covered
    United States
    Description

    This was a cross-sectional nationwide survey of adults in the US conducted between April 24 and May 13, 2020. The survey targeted a representative sample of approximately 5,000 respondents. The rate of COVID-19 cases and testing, most frequently reported symptoms, symptom severity, treatment received, impact of COVID-19 on mental and physical health, and factors predictive of testing positive were assessed.

  11. COVID-19 Trends in Each Country

    • hub.arcgis.com
    • coronavirus-resources.esri.com
    • +2more
    Updated Mar 28, 2020
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Urban Observatory by Esri (2020). COVID-19 Trends in Each Country [Dataset]. https://hub.arcgis.com/maps/a16bb8b137ba4d8bbe645301b80e5740
    Explore at:
    Dataset updated
    Mar 28, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Urban Observatory by Esri
    Area covered
    Earth
    Description

    On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: World Health Organization (WHO)For more information, visit the Johns Hopkins Coronavirus Resource Center.COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.DOI: https://doi.org/10.6084/m9.figshare.125529863/7/2022 - Adjusted the rate of active cases calculation in the U.S. to reflect the rates of serious and severe cases due nearly completely dominant Omicron variant.6/24/2020 - Expanded Case Rates discussion to include fix on 6/23 for calculating active cases.6/22/2020 - Added Executive Summary and Subsequent Outbreaks sectionsRevisions on 6/10/2020 based on updated CDC reporting. This affects the estimate of active cases by revising the average duration of cases with hospital stays downward from 30 days to 25 days. The result shifted 76 U.S. counties out of Epidemic to Spreading trend and no change for national level trends.Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Correction on 6/1/2020Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Revisions added on 4/30/2020 are highlighted.Revisions added on 4/23/2020 are highlighted.Executive SummaryCOVID-19 Trends is a methodology for characterizing the current trend for places during the COVID-19 global pandemic. Each day we assign one of five trends: Emergent, Spreading, Epidemic, Controlled, or End Stage to geographic areas to geographic areas based on the number of new cases, the number of active cases, the total population, and an algorithm (described below) that contextualize the most recent fourteen days with the overall COVID-19 case history. Currently we analyze the countries of the world and the U.S. Counties. The purpose is to give policymakers, citizens, and analysts a fact-based data driven sense for the direction each place is currently going. When a place has the initial cases, they are assigned Emergent, and if that place controls the rate of new cases, they can move directly to Controlled, and even to End Stage in a short time. However, if the reporting or measures to curtail spread are not adequate and significant numbers of new cases continue, they are assigned to Spreading, and in cases where the spread is clearly uncontrolled, Epidemic trend.We analyze the data reported by Johns Hopkins University to produce the trends, and we report the rates of cases, spikes of new cases, the number of days since the last reported case, and number of deaths. We also make adjustments to the assignments based on population so rural areas are not assigned trends based solely on case rates, which can be quite high relative to local populations.Two key factors are not consistently known or available and should be taken into consideration with the assigned trend. First is the amount of resources, e.g., hospital beds, physicians, etc.that are currently available in each area. Second is the number of recoveries, which are often not tested or reported. On the latter, we provide a probable number of active cases based on CDC guidance for the typical duration of mild to severe cases.Reasons for undertaking this work in March of 2020:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-25 days + 5% from past 26-49 days - total deaths. On 3/17/2022, the U.S. calculation was adjusted to: Active Cases = 100% of new cases in past 14 days + 6% from past 15-25 days + 3% from past 26-49 days - total deaths. Sources: https://www.cdc.gov/mmwr/volumes/71/wr/mm7104e4.htm https://covid.cdc.gov/covid-data-tracker/#variant-proportions If a new variant arrives and appears to cause higher rates of serious cases, we will roll back this adjustment. We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source

  12. Average number of daily new infections in the simulation for the first 226...

    • plos.figshare.com
    xlsx
    Updated Jun 4, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Pinar Keskinocak; Buse Eylul Oruc; Arden Baxter; John Asplund; Nicoleta Serban (2023). Average number of daily new infections in the simulation for the first 226 days. [Dataset]. http://doi.org/10.1371/journal.pone.0239798.s018
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Pinar Keskinocak; Buse Eylul Oruc; Arden Baxter; John Asplund; Nicoleta Serban
    License

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

    Description

    Data values are averages from the 30 runs. The number of children, adults, elderly and total number of people infected on a given day in the simulation is recorded. Since the simulation is based on the population of Georgia, and each entity in the simulation represents ten people, all data values recorded are based on one-tenth of the population of Georgia, that is, there are a total of roughly one million people in the simulation. (XLSX)

  13. New York State cumulative COVID-19 cases from Mar. 4, 2020 - Mar. 7, 2021,...

    • statista.com
    Updated Jul 27, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2022). New York State cumulative COVID-19 cases from Mar. 4, 2020 - Mar. 7, 2021, by day [Dataset]. https://www.statista.com/statistics/1109721/new-york-state-covid-cumulative-cases-us/
    Explore at:
    Dataset updated
    Jul 27, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Mar 4, 2020 - Mar 7, 2021
    Area covered
    New York, United States
    Description

    The number of positive COVID-19 cases in the state of New York reached almost 1.7 million by March 7, 2021. The first case of the novel coronavirus in New York State was confirmed at the start of March 2020.

    New Yorkers urged to work from home The coronavirus has caused widespread suffering, and New York is among the states with the highest number of COVID-19 cases in the United States. A quick and aggressive response to the initial outbreak was required as the virus stretched hospitals in New York State to their maximum capacity. In mid-March 2020, Governor Andrew Cuomo ordered all workers in non-essential businesses to work from home. As part of the state’s reopening plans in 2021, remote work continues to be encouraged and remains one of the best ways to prevent the spread of COVID-19.

    The importance of coronavirus testing As of March 6, 2021, there had been almost 40 million COVID-19 tests in New York State, meaning it was one of the states with the highest number of coronavirus tests conducted in the U.S. Widespread testing, which also includes testing people with no symptoms, is essential for tracking the true spread of the coronavirus. By identifying more positive infections, health authorities can quickly manage the areas with high cases, and states can avoid tighter restrictions in the weeks and months ahead.

  14. f

    Hospital bed, ICU bed, and ventilator demand for SC.

    • plos.figshare.com
    xlsx
    Updated Jun 14, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Pinar Keskinocak; Buse Eylul Oruc; Arden Baxter; John Asplund; Nicoleta Serban (2023). Hospital bed, ICU bed, and ventilator demand for SC. [Dataset]. http://doi.org/10.1371/journal.pone.0239798.s007
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Jun 14, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Pinar Keskinocak; Buse Eylul Oruc; Arden Baxter; John Asplund; Nicoleta Serban
    License

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

    Description

    The number of people that need hospital beds, ICU beds, and ventilators is recorded for each hospital region under SC. (XLSX)

  15. Hospital bed, ICU bed, and ventilator demand for Scenario 8.

    • plos.figshare.com
    xlsx
    Updated Jun 14, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Pinar Keskinocak; Buse Eylul Oruc; Arden Baxter; John Asplund; Nicoleta Serban (2023). Hospital bed, ICU bed, and ventilator demand for Scenario 8. [Dataset]. http://doi.org/10.1371/journal.pone.0239798.s015
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Jun 14, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Pinar Keskinocak; Buse Eylul Oruc; Arden Baxter; John Asplund; Nicoleta Serban
    License

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

    Description

    The number of people that need hospital beds, ICU beds, and ventilators is recorded for each hospital region under Scenario 8. (XLSX)

  16. f

    Data from: The first hundred days of COVID-19 in Pernambuco State, Brazil:...

    • scielo.figshare.com
    • datasetcatalog.nlm.nih.gov
    jpeg
    Updated Jun 1, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Wayner Vieira de Souza; Celina Maria Turchi Martelli; Amanda Priscila de Santana Cabral Silva; Lívia Teixeira de Souza Maia; Maria Cynthia Braga; Luciana Caroline Albuquerque Bezerra; George Santiago Dimech; Ulisses Ramos Montarroyos; Thalia Velho Barreto de Araújo; Demócrito de Barros Miranda-Filho; Ricardo Arraes de Alencar Ximenes; Maria de Fátima Pessoa Militão de Albuquerque (2023). The first hundred days of COVID-19 in Pernambuco State, Brazil: epidemiology in historical context [Dataset]. http://doi.org/10.6084/m9.figshare.14280878.v1
    Explore at:
    jpegAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    SciELO journals
    Authors
    Wayner Vieira de Souza; Celina Maria Turchi Martelli; Amanda Priscila de Santana Cabral Silva; Lívia Teixeira de Souza Maia; Maria Cynthia Braga; Luciana Caroline Albuquerque Bezerra; George Santiago Dimech; Ulisses Ramos Montarroyos; Thalia Velho Barreto de Araújo; Demócrito de Barros Miranda-Filho; Ricardo Arraes de Alencar Ximenes; Maria de Fátima Pessoa Militão de Albuquerque
    License

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

    Area covered
    Brazil, State of Pernambuco
    Description

    Abstract: The timeline of the COVID-19 pandemic began on December 31, 2019, in China, with SARS-CoV-2 identified as the etiological agent. This article aims to describe the COVID-19 epidemic’s spatial and temporal dynamics in the first hundred days in the State of Pernambuco, Brazil. We present the evolution in cases and deaths according to epidemiological weeks. We analyzed the series of accumulated daily confirmed COVID-19 cases, with projections for the subsequent 15 days, using the JoinPoint app. This software allows identifying turning points, testing their statistical significance. We also analyze the trend in the spread of COVID-19 to the interior of the state, considering the percent distribution of cases in the state capital, Recife, municipalities in Greater Metropolitan Recife, and the state’s interior, by sets of three weeks, constructing thematic maps. The first hundred days of the COVID-19 epidemic resulted in 52,213 cases and 4,235 deaths from March 12, or epidemiological week 11, until June 20, 2020 (epidemiological week 25). The peak in the epidemic curve occurred in epidemiological week 21 (May 23), followed by deceleration in the number of cases. We initially detected the spread of cases from the city center to the periphery of the state capital and Metropolitan Area, followed by rapid spread to the state’s interior. There was a decrease in the mean daily growth starting in April, but with an average threshold of more than 6,000 weekly cases of COVID-19. At the end of the period, the state’s case series indicates the persistence of SARS-CoV-2 circulation and community transmission. Finally, paraphrasing Gabriel Garcia Marques in One Hundred Years of Solitude, we ask whether we are facing “a pause in the storm or a sign of redoubled rain”.

  17. m

    COVID-19 reporting

    • mass.gov
    Updated Mar 4, 2020
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Executive Office of Health and Human Services (2020). COVID-19 reporting [Dataset]. https://www.mass.gov/info-details/covid-19-reporting
    Explore at:
    Dataset updated
    Mar 4, 2020
    Dataset provided by
    Executive Office of Health and Human Services
    Department of Public Health
    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.

  18. Number of U.S. COVID-19 cases from Jan. 20, 2020 - Nov. 11, 2022, by week

    • statista.com
    Updated Nov 17, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2022). Number of U.S. COVID-19 cases from Jan. 20, 2020 - Nov. 11, 2022, by week [Dataset]. https://www.statista.com/statistics/1102816/coronavirus-covid19-cases-number-us-americans-by-day/
    Explore at:
    Dataset updated
    Nov 17, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 20, 2020 - Nov 11, 2022
    Area covered
    United States
    Description

    Around 282 thousand new cases of COVID-19 were reported in the United States during the week ending November 11, 2022. Between January 20, 2020 and November 11, 2022 there had been around 96.8 million confirmed cases of COVID-19 with over one million deaths in the U.S. as reported by the World Health Organization.

    How did the coronavirus outbreak start? Pneumonia cases with an unknown cause were first reported in the Hubei province of China at the end of December 2019. Patients described symptoms including a fever and difficulty breathing, and early reports suggested no evidence of human-to-human transmission. We now know that a novel coronavirus named SARS-CoV-2 is causing the disease COVID-19. The virus has been characterized as a pandemic and continues to spread from person to person – there have been around 642 million cases worldwide as of November 17, 2022.

    The importance of isolation and quarantine In an effort to contain the early spread of the virus, China tightened travel restrictions and enforced isolation measures in the hardest-hit areas. The World Health Organization endorsed this strategy, and countries around the world implemented similar quarantine measures. Staying at home can limit the spread of the virus, and this applies to individuals who are only showing mild symptoms or none at all. Asymptomatic carriers of the virus – those that are experiencing no symptoms – may transmit the virus to people who are at a higher risk of getting very sick.

  19. Number of COVID-19 deaths in the United States as of March 10, 2023, by...

    • statista.com
    Updated Mar 28, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2023). Number of COVID-19 deaths in the United States as of March 10, 2023, by state [Dataset]. https://www.statista.com/statistics/1103688/coronavirus-covid19-deaths-us-by-state/
    Explore at:
    Dataset updated
    Mar 28, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    As of March 10, 2023, there have been 1.1 million deaths related to COVID-19 in the United States. There have been 101,159 deaths in the state of California, more than any other state in the country – California is also the state with the highest number of COVID-19 cases.

    The vaccine rollout in the U.S. Since the start of the pandemic, the world has eagerly awaited the arrival of a safe and effective COVID-19 vaccine. In the United States, the immunization campaign started in mid-December 2020 following the approval of a vaccine jointly developed by Pfizer and BioNTech. As of March 22, 2023, the number of COVID-19 vaccine doses administered in the U.S. had reached roughly 673 million. The states with the highest number of vaccines administered are California, Texas, and New York.

    Vaccines achieved due to work of research groups Chinese authorities initially shared the genetic sequence to the novel coronavirus in January 2020, allowing research groups to start studying how it invades human cells. The surface of the virus is covered with spike proteins, which enable it to bind to human cells. Once attached, the virus can enter the cells and start to make people ill. These spikes were of particular interest to vaccine manufacturers because they hold the key to preventing viral entry.

  20. Hospital bed, ICU bed, and ventilator demand for Scenario 3.

    • plos.figshare.com
    xlsx
    Updated Jun 4, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Pinar Keskinocak; Buse Eylul Oruc; Arden Baxter; John Asplund; Nicoleta Serban (2023). Hospital bed, ICU bed, and ventilator demand for Scenario 3. [Dataset]. http://doi.org/10.1371/journal.pone.0239798.s010
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Pinar Keskinocak; Buse Eylul Oruc; Arden Baxter; John Asplund; Nicoleta Serban
    License

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

    Description

    The number of people that need hospital beds, ICU beds, and ventilators is recorded for each hospital region under Scenario 3. (XLSX)

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
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.

Search
Clear search
Close search
Google apps
Main menu