37 datasets found
  1. T

    China Coronavirus COVID-19 Cases

    • tradingeconomics.com
    csv, excel, json, xml
    Updated May 29, 2022
    + more versions
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    TRADING ECONOMICS (2022). China Coronavirus COVID-19 Cases [Dataset]. https://tradingeconomics.com/china/coronavirus-cases
    Explore at:
    excel, csv, xml, jsonAvailable download formats
    Dataset updated
    May 29, 2022
    Dataset authored and provided by
    TRADING ECONOMICS
    License

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

    Time period covered
    Jan 4, 2020 - May 17, 2023
    Area covered
    China
    Description

    China recorded 99256991 Coronavirus Cases since the epidemic began, according to the World Health Organization (WHO). In addition, China reported 5226 Coronavirus Deaths. This dataset includes a chart with historical data for China Coronavirus Cases.

  2. Coronavirus (COVID-19) dataset

    • kaggle.com
    Updated Apr 29, 2020
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    Balaaje (2020). Coronavirus (COVID-19) dataset [Dataset]. https://www.kaggle.com/balaaje/coronavirus-covid19-dataset/discussion
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 29, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Balaaje
    Description

    Context

    The 2019–20 coronavirus pandemic is an ongoing global pandemic of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus first emerged in Wuhan, Hubei, China, in December 2019. On 11 March 2020, the World Health Organization declared the outbreak a pandemic. As of 11 March 2020, over 126,000 cases have been confirmed in more than 110 countries and territories, with major outbreaks in mainland China, Italy, South Korea, and Iran. More than 4,600 have died from the disease and 67,000 have recovered.

    Content

    2019 Novel Coronavirus (2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. Early on, many of the patients in the outbreak in Wuhan, China reportedly had some link to a large seafood and animal market, suggesting animal-to-person spread. However, a growing number of patients reportedly have not had exposure to animal markets, indicating person-to-person spread is occurring. At this time, it’s unclear how easily or sustainably this virus is spreading between people - CDC

    This dataset has information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this data was scrapped from https://www.worldometers.info/coronavirus/.This data is solely for education purposes only.

    Acknowledgements

    This data is solely belongs to https://www.worldometers.info/coronavirus/. for licensing visit https://www.worldometers.info/licensing/

  3. COVID-19 Worldwide Daily Data

    • kaggle.com
    Updated Aug 28, 2020
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    Altadata (2020). COVID-19 Worldwide Daily Data [Dataset]. https://www.kaggle.com/altadata/covid19/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 28, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Altadata
    Description

    https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5505749%2F2b83271d61e47e2523e10dc9c28e545c%2F600x200.jpg?generation=1599042483103679&alt=media" alt="">

    ALTADATA is a curated data marketplace where our subscribers and our data partners can easily exchange ready-to-analyze datasets and create insights with EPO, our visual data analytics platform.

    COVID-19 Worldwide Daily Data

    Daily global COVID-19 data for all countries, provided by Johns Hopkins University (JHU) Center for Systems Science and Engineering (CSSE). If you want to use the update version of the data, you can use our daily updated data with the help of api key by entering it via Altadata.

    Overview

    In this data product, you may find the latest and historical global daily data on the COVID-19 pandemic for all countries.

    The COVID‑19 pandemic, also known as the coronavirus pandemic, is an ongoing global pandemic of coronavirus disease 2019 (COVID‑19), caused by severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2). The outbreak was first identified in December 2019 in Wuhan, China. The World Health Organization declared the outbreak a Public Health Emergency of International Concern on 30 January 2020 and a pandemic on 11 March. As of 12 August 2020, more than 20.2 million cases of COVID‑19 have been reported in more than 188 countries and territories, resulting in more than 741,000 deaths; more than 12.5 million people have recovered.

    The Johns Hopkins Coronavirus Resource Center is a continuously updated source of COVID-19 data and expert guidance. They aggregate and analyze the best data available on COVID-19 - including cases, as well as testing, contact tracing and vaccine efforts - to help the public, policymakers and healthcare professionals worldwide respond to the pandemic.

    Methodology

    • Cases and Death counts include confirmed and probable (where reported)
    • Recovered cases are estimates based on local media reports, and state and local reporting when available, and therefore may be substantially lower than the true number. US state-level recovered cases are from COVID Tracking Project.
    • Active cases = total cases - total recovered - total deaths
    • Incidence Rate = cases per 100,000 persons
    • Case-Fatality Ratio (%) = Number recorded deaths / Number cases
    • Country Population represents 2019 projections by UN Population Division, integrated to the JHU CSSE's COVID-19 data by ALTADATA

    Data Source

    Related Data Products

    Suggested Blog Posts

    Data Dictionary

    • Reported Date (reported_date) : Covid-19 Report Date
    • Country_Region (country_region) : Country, region or sovereignty name
    • Population (population) : Country populations as per United Nations Population Division
    • Confirmed Case (confirmed) : Confirmed cases include presumptive positive cases and probable cases
    • Active cases (active) : Active cases = total confirmed - total recovered - total deaths
    • Deaths (deaths) : Death cases counts
    • Recovered (recovered) : Recovered cases counts
    • Mortality Rate (mortality_rate) : Number of recorded deaths * 100 / Number of confirmed cases
    • Incident Rate (incident_rate) : Confirmed cases per 100,000 persons
  4. C

    China CN: COVID-19: No of Death: ytd: Hubei: Wuhan

    • ceicdata.com
    Updated Feb 15, 2025
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    CEICdata.com (2025). China CN: COVID-19: No of Death: ytd: Hubei: Wuhan [Dataset]. https://www.ceicdata.com/en/china/covid19-no-of-death/cn-covid19-no-of-death-ytd-hubei-wuhan
    Explore at:
    Dataset updated
    Feb 15, 2025
    Dataset provided by
    CEICdata.com
    License

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

    Time period covered
    Dec 2, 2022 - Dec 13, 2022
    Area covered
    China
    Description

    COVID-19: Number of Death: Year to Date: Hubei: Wuhan data was reported at 3,869.000 Person in 13 Dec 2022. This stayed constant from the previous number of 3,869.000 Person for 12 Dec 2022. COVID-19: Number of Death: Year to Date: Hubei: Wuhan data is updated daily, averaging 3,869.000 Person from Jan 2020 (Median) to 13 Dec 2022, with 1069 observations. The data reached an all-time high of 3,869.000 Person in 13 Dec 2022 and a record low of 1.000 Person in 14 Jan 2020. COVID-19: Number of Death: Year to Date: Hubei: Wuhan data remains active status in CEIC and is reported by National Health Commission. The data is categorized under High Frequency Database’s Disease Outbreaks – Table CN.GZ: COVID-19: No of Death. Clinical diagnosis included in since 12Feb 自2月12日起纳入临床诊断

  5. COVID-19 Country Level Timeseries

    • kaggle.com
    Updated Mar 29, 2020
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    Arpan Das (2020). COVID-19 Country Level Timeseries [Dataset]. https://www.kaggle.com/arpandas65/covid19-country-level-timeseries/metadata
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 29, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Arpan Das
    License

    http://opendatacommons.org/licenses/dbcl/1.0/http://opendatacommons.org/licenses/dbcl/1.0/

    Description

    Context

    Amidst the COVID-19 outbreak, the world is facing great crisis in every way. The value and things we built as a human race are going through tremendous challenges. It is a very small effort to bring curated data set on Novel Corona Virus to accelerate the forecasting and analytical experiments to cope up with this critical situation. It will help to visualize the country level out break and to keep track on regularly added new incidents.

    COVID-19 Country Level Timeseries Dataset

    This Dataset contains country wise public domain time series information on COVID-19 outbreak. The Data is sorted alphabetically on Country name and Date of Observation.

    Column Descriptions

    The data set contains the following columns:
    ObservationDate: The date on which the incidents are observed country: Country of the Outbreak Confirmed: Number of confirmed cases till observation date Deaths: Number of death cases till observation date Recovered: Number of recovered cases till observation date New Confirmed: Number of new confirmed cases on observation date New Deaths: Number of New death cases on observation date New Recovered: Number of New recovered cases on observation date latitude: Latitude of the affected country longitude: Longitude of the affected country

    Acknowledgements

    This data set is a cleaner version of the https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset data set with added geo location information and regularly added incident counts. I would like to thank this great effort by SRK.

    Original Data Source

    Johns Hopkins University MoBS lab - https://www.mobs-lab.org/2019ncov.html World Health Organization (WHO): https://www.who.int/ DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia. BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/ National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html Macau Government: https://www.ssm.gov.mo/portal/ Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0 US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19 Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus

  6. Covid-19 Worldometer Dataset

    • kaggle.com
    Updated Apr 11, 2020
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    Vaibhav Panchal (2020). Covid-19 Worldometer Dataset [Dataset]. https://www.kaggle.com/pvaibhav1995/covid19-worldometer-dataset/kernels
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 11, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Vaibhav Panchal
    Description

    UPDATED till 10/04/2020 23:59:59

    Context

    Worldometer Covid-19 Data is available as csv file. Uploading it here for using it in Kaggle kernels and getting insights from the broader DS community.

    Content

    (2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. Early on, many of the patients in the outbreak in Wuhan, China reportedly had some link to a large seafood and animal market, suggesting animal-to-person spread. However, a growing number of patients reportedly have not had exposure to animal markets, indicating person-to-person spread is occurring. At this time, it’s unclear how easily or sustainably this virus is spreading between people - CDC

    Acknowledgements

    Country - List of countries affected by covid-19 Total Cases - Cumulative number of confirmed cases till date New Cases - New confirmed cases each day Total Deaths - Cumulative number of deaths till date New Deaths - New death cases each day Total Recovered - Cumulative number of recovered cases till date Active Cases - Cumulative number of recovered cases till date Serious, Critical - Cumulative number of Serious/Critical cases till date Tot Cases/1M pop - Cumulative number of confirmed cases till date per million population Deaths/1M pop - Cumulative number of deaths till date per million population Total Tests - Cumulative number of test till date Tests/1M pop - Cumulative number of test till date per million population

    Acknowledgements

  7. Coronavirus COVID-19 Global Cases

    • redivis.com
    application/jsonl +7
    Updated Jul 13, 2020
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    Stanford Center for Population Health Sciences (2020). Coronavirus COVID-19 Global Cases [Dataset]. http://doi.org/10.57761/pyf5-4e40
    Explore at:
    sas, csv, application/jsonl, spss, stata, parquet, arrow, avroAvailable download formats
    Dataset updated
    Jul 13, 2020
    Dataset provided by
    Redivis Inc.
    Authors
    Stanford Center for Population Health Sciences
    Time period covered
    Jan 22, 2020 - Jul 12, 2020
    Description

    Abstract

    JHU Coronavirus COVID-19 Global Cases, by country

    Documentation

    PHS is updating the Coronavirus Global Cases dataset weekly, Monday, Wednesday and Friday from Cloud Marketplace.

    This data comes from the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). This database was created in response to the Coronavirus public health emergency to track reported cases in real-time. The data include the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries, aggregated at the appropriate province or state. It was developed to enable researchers, public health authorities and the general public to track the outbreak as it unfolds. Additional information is available in the blog post.

    Visual Dashboard (desktop): https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

    Section 2

    Included Data Sources are:

    %3C!-- --%3E

    Section 3

    **Terms of Use: **

    This GitHub repo and its contents herein, including all data, mapping, and analysis, copyright 2020 Johns Hopkins University, all rights reserved, is provided to the public strictly for educational and academic research purposes. The Website relies upon publicly available data from multiple sources, that do not always agree. The Johns Hopkins University hereby disclaims any and all representations and warranties with respect to the Website, including accuracy, fitness for use, and merchantability. Reliance on the Website for medical guidance or use of the Website in commerce is strictly prohibited.

    Section 4

    **U.S. county-level characteristics relevant to COVID-19 **

    Chin, Kahn, Krieger, Buckee, Balsari and Kiang (forthcoming) show that counties differ significantly in biological, demographic and socioeconomic factors that are associated with COVID-19 vulnerability. A range of publicly available county-specific data identifying these key factors, guided by international experiences and consideration of epidemiological parameters of importance, have been combined by the authors and are available for use:

    https://github.com/mkiang/county_preparedness/

  8. COVID-19

    • kaggle.com
    • data.world
    zip
    Updated May 25, 2020
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    Atila Madai (2020). COVID-19 [Dataset]. https://www.kaggle.com/atilamadai/covid19
    Explore at:
    zip(68606230 bytes)Available download formats
    Dataset updated
    May 25, 2020
    Authors
    Atila Madai
    License

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

    Description

    Context

    The novel coronavirus that has infected more than 79,551 people worldwide (as of time of writing this context) is spreading rapidly, and independently, in countries outside of China, including Italy, South Korea, and Iran. The viral illness is being diagnosed among hundreds of people in South Korea, Italy and Iran who have no connection to China.

    Content

    In the notebook I use the time series data. Time series data columns are described in the column description.

    Acknowledgements

    Thanks to the Johns Hopkins University for providing this data-set for educational purposes. https://github.com/CSSEGISandData/COVID-19

    Inspiration

    To visualize COVID-19 spread world wide.

  9. z

    Counts of COVID-19 reported in CHINA: 2019-2021

    • zenodo.org
    • catalog.midasnetwork.us
    • +2more
    json, xml, zip
    Updated Jun 3, 2024
    + more versions
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    MIDAS Coordination Center; MIDAS Coordination Center (2024). Counts of COVID-19 reported in CHINA: 2019-2021 [Dataset]. http://doi.org/10.25337/t7/ptycho.v2.0/cn.840539006
    Explore at:
    json, xml, zipAvailable download formats
    Dataset updated
    Jun 3, 2024
    Dataset provided by
    Project Tycho
    Authors
    MIDAS Coordination Center; MIDAS Coordination Center
    License

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

    Time period covered
    Dec 30, 2019 - Jul 31, 2021
    Area covered
    China
    Description

    Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team, except for aggregation of individual case count data into daily counts when that was the best data available for a disease and location. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format. All geographic locations at the country and admin1 level have been represented at the same geographic level as in the data source, provided an ISO code or codes could be identified, unless the data source specifies that the location is listed at an inaccurate geographical level. For more information about decisions made by the curation team, recommended data processing steps, and the data sources used, please see the README that is included in the dataset download ZIP file.

  10. T

    China Coronavirus COVID-19 Recovered

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Mar 11, 2020
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    TRADING ECONOMICS (2020). China Coronavirus COVID-19 Recovered [Dataset]. https://tradingeconomics.com/china/coronavirus-recovered
    Explore at:
    xml, json, csv, excelAvailable download formats
    Dataset updated
    Mar 11, 2020
    Dataset authored and provided by
    TRADING ECONOMICS
    License

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

    Time period covered
    Dec 31, 2019 - Dec 15, 2021
    Area covered
    China
    Description

    China recorded 86689 Coronavirus Recovered since the epidemic began, according to the World Health Organization (WHO). In addition, China reported 4636 Coronavirus Deaths. This dataset includes a chart with historical data for China Coronavirus Recovered.

  11. m

    MDCOVID19 TotalProbableDeathsByDateOfDeath

    • data.imap.maryland.gov
    • coronavirus.maryland.gov
    • +2more
    Updated May 22, 2020
    + more versions
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    ArcGIS Online for Maryland (2020). MDCOVID19 TotalProbableDeathsByDateOfDeath [Dataset]. https://data.imap.maryland.gov/datasets/50036695d3744b7ba2bc94f66dd76355
    Explore at:
    Dataset updated
    May 22, 2020
    Dataset authored and provided by
    ArcGIS Online for Maryland
    Area covered
    Description

    SummaryThe cumulative number of probable COVID-19 deaths among Maryland residents, by date of death.DescriptionThe MD COVID-19 - Total Probable Deaths by Date of Death data layer is a collection of the statewide probable COVID-19 related deaths that have been reported each day by the Vital Statistics Administration by date of death. A death is classified as probable if the person's death certificate notes COVID-19 to be a probable, suspect or presumed cause or condition. Probable deaths are not yet been confirmed by a laboratory test. Some data on deaths may be unavailable due to the time lag between the death, typically reported by a hospital or other facility, and the submission of the complete death certificate. Confirmed deaths are available from the MD COVID-19 - Total Confirmed Deaths by Date of Death data layer.COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county.

  12. f

    Epidemiological data on the novel coronavirus 2019-nCoV infection cases in...

    • datasetcatalog.nlm.nih.gov
    Updated Jun 23, 2020
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    Xie, Min; Liu, Jun; Yang, Qin; Luo, Wei; Guo, Limin; Duan, Qinwei; Liu, Xi; Wu, Ying; Zhu, Rong; Feng, Shipin; Wang, Li; Li, Jia (2020). Epidemiological data on the novel coronavirus 2019-nCoV infection cases in China [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0000578958
    Explore at:
    Dataset updated
    Jun 23, 2020
    Authors
    Xie, Min; Liu, Jun; Yang, Qin; Luo, Wei; Guo, Limin; Duan, Qinwei; Liu, Xi; Wu, Ying; Zhu, Rong; Feng, Shipin; Wang, Li; Li, Jia
    Area covered
    China
    Description

    This data record contains one dataset deta of 2019-nCOV in China.xlsx, in .xlsx file format.The dataset includes the following information (in 8 separate columns) on the novel coronavirus (2019-nCoV) infection cases in China:-total number of confirmed cases, -total number of suspected cases-total number of cured cases-total number of deaths-total number of new confirmed cases-total number of new suspected cases-total number of new cured cases-total number of new deathsThe number of cases are reported for each day from January 20th to February 21st 2020.Study background, aims and methodology: The 2019–20 coronavirus outbreak is an ongoing public health emergency of international concern involving coronavirus disease 2019 (COVID-19). At the end of December 2019, the epidemic of the novel coronavirus 2019-nCOV infection has spread from the initial place of Wuhan, Huibei province in China, resulting in an epidemic throughout China, with sporadic cases reported globally.The elderly, as well as people with primary diseases, are more likely to die from the infection. Children with chronic kidney disease (CKD), and children on dialysis, are vulnerable, due to their primary diseases and low immunity, especially those who suffer from long-term hormone, immunosuppressive therapy, and maintenance hemodialysis.The aim of this study was to analyse the epidemiological and clinical characteristics of the novel coronavirus, and to explore the infection prevention and control strategies of 2019-nCoV in children with chronic kidney disease (CKD) and children on dialysis.Data were collected from the 2019-nCoV management plan of the National Health Commission of the People’s Republic of China and relevant guidelines. Data on the COVID-19 cases in China, including the number of people, clinical characteristics, effective prevention and control measures from January 20th to February 21st, 2020, and statistical data on CKD in children were collected.

  13. S

    COVID-19 Wider Impacts - Excess Deaths

    • find.data.gov.scot
    csv
    Updated Oct 5, 2023
    + more versions
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    National Records of Scotland (2023). COVID-19 Wider Impacts - Excess Deaths [Dataset]. https://find.data.gov.scot/datasets/19559
    Explore at:
    csv(0.6786 MB), csv(1.1421 MB), csv(0.0262 MB)Available download formats
    Dataset updated
    Oct 5, 2023
    Dataset provided by
    National Records of Scotland
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    Novel coronavirus (COVID-19) is a new strain of coronavirus first identified in Wuhan, China. Clinical presentation may range from mild-to-moderate illness to pneumonia or severe acute respiratory infection. The COVID-19 pandemic has wider impacts on individuals' health, and their use of healthcare services, than those that occur as the direct result of infection. Reasons for this may include: * Individuals being reluctant to use health services because they do not want to burden the NHS or are anxious about the risk of infection. * The health service delaying preventative and non-urgent care such as some screening services and planned surgery. * Other indirect effects of interventions to control COVID-19, such as mental or physical consequences of distancing measures. This dataset provides information on trend data regarding the wider impact of the pandemic on the number of deaths in Scotland, derived from the National Records of Scotland (NRS) weekly deaths registration data. Data show recent trends in deaths (2020), whether COVID or non-COVID related, and historic trends for comparison (five-year average, 2015-2019). The recent trend data are shown by age group and sex, and the national data are also shown by broad area deprivation category (Scottish Index of Multiple Deprivation, SIMD). This data is also available on the COVID-19 Wider Impact Dashboard. Additional data sources relating to this topic area are provided in the Links section of the Metadata below. Information on COVID-19, including stay at home advice for people who are self-isolating and their households, can be found on NHS Inform. All publications and supporting material to this topic area can be found in the weekly COVID-19 Statistical Report. The date of the next release can be found on our list of forthcoming publications.

  14. ARCHIVED - Weekly COVID-19 Statistical Data in Scotland

    • dtechtive.com
    • find.data.gov.scot
    csv
    Updated Dec 22, 2022
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    Public Health Scotland (2022). ARCHIVED - Weekly COVID-19 Statistical Data in Scotland [Dataset]. https://dtechtive.com/datasets/19628
    Explore at:
    csv(0.0537 MB), csv(0.0008 MB), csv(0.0535 MB), csv(0.014 MB), csv(0.1093 MB), csv(0.0265 MB), csv(0.0016 MB), csv(0.0022 MB), csv(0.0729 MB), csv(0.0026 MB), csv(0.0038 MB), csv(0.4845 MB), csv(0.0296 MB), csv(0.0126 MB), csv(0.0732 MB), csv(0.0005 MB), csv(0.0553 MB), csv(0.0002 MB), csv(0.0015 MB), csv(0.0348 MB), csv(0.033 MB), csv(0.0304 MB), csv(0.0551 MB), csv(0.0112 MB), csv(0.0037 MB), csv(0.0317 MB), csv(0.109 MB), csv(0.002 MB), csv(0.0192 MB)Available download formats
    Dataset updated
    Dec 22, 2022
    Dataset provided by
    Public Health Scotland
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Area covered
    Scotland
    Description

    This open data publication has moved to COVID-19 Statistical Data in Scotland (from 02/11/2022) Novel coronavirus (COVID-19) is a new strain of coronavirus first identified in Wuhan, China. Clinical presentation may range from mild-to-moderate illness to pneumonia or severe acute respiratory infection. This dataset provides information on demographic characteristics (age, sex, deprivation) of confirmed novel coronavirus (COVID-19) cases, as well as trend data regarding the wider impact of the virus on the healthcare system. Data includes information on primary care out of hours consultations, respiratory calls made to NHS24, contact with COVID-19 Hubs and Assessment Centres, incidents received by Scottish Ambulance Services (SAS), as well as COVID-19 related hospital admissions and admissions to ICU (Intensive Care Unit). Further data on the wider impact of the COVID-19 response, focusing on hospital admissions, unscheduled care and volume of calls to NHS24, is available on the COVID-19 Wider Impact Dashboard. There is a large amount of data being regularly published regarding COVID-19 (for example, Coronavirus in Scotland - Scottish Government and Deaths involving coronavirus in Scotland - National Records of Scotland. Additional data sources relating to this topic area are provided in the Links section of the Metadata below. Information on COVID-19, including stay at home advice for people who are self-isolating and their households, can be found on NHS Inform. All publications and supporting material to this topic area can be found in the weekly COVID-19 Statistical Report. The date of the next release can be found on our list of forthcoming publications. Data visualisation is available to view in the interactive dashboard accompanying the COVID-19 Statistical Report. Please note information on COVID-19 in children and young people of educational age, education staff and educational settings is presented in a new COVID-19 Education Surveillance dataset going forward.

  15. MD COVID19 Congregate Cases and Deaths Total Summary

    • data-maryland.opendata.arcgis.com
    • data.imap.maryland.gov
    • +2more
    Updated Nov 30, 2020
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    ArcGIS Online for Maryland (2020). MD COVID19 Congregate Cases and Deaths Total Summary [Dataset]. https://data-maryland.opendata.arcgis.com/datasets/d50ae11a0494498886c5b6bb4513a045
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    Dataset updated
    Nov 30, 2020
    Dataset provided by
    https://arcgis.com/
    Authors
    ArcGIS Online for Maryland
    Description

    SummaryTotal ever COVID-19 cases and deaths at Maryland congregate living facilities.DescriptionDeprecated as of November 17, 2021.The Outbreak-Associated Cases in Congregate Living data dashboard on coronavirus.maryland.gov was redesigned on 11/17/21 to align with other outbreak reporting. Visit MD COVID-19 Congregate Outbreaks to view Outbreak-Associated Cases in Congregate Living data as reported after 11/17/21.The MD COVID-19 Congregate Cases and Deaths total Summary data layer is the cumulative total of COVID-19 cases and deaths that have occured in nursing homes, assisted living facilities, group homes of 10 or more and state and local facilities. Data are reported to MDH by local health departments, the Department of Public Safety and Correctional Services and the Department of Juvenile Services and are updated once weekly.COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county.

  16. COVID-19 Trends in Each Country

    • data.amerigeoss.org
    esri rest, html
    Updated Jul 29, 2020
    + more versions
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    ESRI (2020). COVID-19 Trends in Each Country [Dataset]. https://data.amerigeoss.org/dataset/covid-19-trends-in-each-country
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    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

  17. j

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

    • systems.jhu.edu
    • github.com
    • +1more
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    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://systems.jhu.edu/research/public-health/ncov/
    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

  18. ARCHIVED - COVID-19 Statistical Data in Scotland

    • find.data.gov.scot
    • dtechtive.com
    csv
    Updated Oct 12, 2023
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    Public Health Scotland (2023). ARCHIVED - COVID-19 Statistical Data in Scotland [Dataset]. https://find.data.gov.scot/datasets/19552
    Explore at:
    csv(0.0732 MB), csv(0.0419 MB), csv(0.0418 MB), csv(0.0192 MB), csv(0.1093 MB), csv(0.0014 MB), csv(5.0432 MB), csv(0.0005 MB), csv(0.0026 MB), csv(0.0332 MB), csv(0.0396 MB), csv(58.4012 MB), csv(0.014 MB), csv(0.109 MB), csv(0.0037 MB), csv(34.9529 MB), csv(4.374 MB), csv(0.121 MB), csv(0.0002 MB), csv(0.6132 MB), csv(0.0126 MB), csv(0.0035 MB), csv(0.0052 MB), csv(0.0269 MB), csv(5.3315 MB), csv(0.0729 MB), csv(0.0019 MB), csv(0.0018 MB), csv(0.0006 MB), csv(0.0091 MB), csv(0.0043 MB), csv(0.0339 MB), csv(0.0402 MB), csv(0.0022 MB), csv(0.0409 MB), csv(0.0112 MB), csv(0.0298 MB), csv(0.0067 MB), csv(0.4505 MB), csv(2.9269 MB)Available download formats
    Dataset updated
    Oct 12, 2023
    Dataset provided by
    Public Health Scotland
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Area covered
    Scotland
    Description

    This publication was archived on 12 October 2023. Please see the Viral Respiratory Diseases (Including Influenza and COVID-19) in Scotland publication for the latest data. This dataset provides information on number of new daily confirmed cases, negative cases, deaths, testing by NHS Labs (Pillar 1) and UK Government (Pillar 2), new hospital admissions, new ICU admissions, hospital and ICU bed occupancy from novel coronavirus (COVID-19) in Scotland, including cumulative totals and population rates at Scotland, NHS Board and Council Area levels (where possible). Seven day positive cases and population rates are also presented by Neighbourhood Area (Intermediate Zone 2011). Information on how PHS publish small are COVID figures is available on the PHS website. Information on demographic characteristics (age, sex, deprivation) of confirmed novel coronavirus (COVID-19) cases, as well as trend data regarding the wider impact of the virus on the healthcare system is provided in this publication. Data includes information on primary care out of hours consultations, respiratory calls made to NHS24, contact with COVID-19 Hubs and Assessment Centres, incidents received by Scottish Ambulance Services (SAS), as well as COVID-19 related hospital admissions and admissions to ICU (Intensive Care Unit). Further data on the wider impact of the COVID-19 response, focusing on hospital admissions, unscheduled care and volume of calls to NHS24, is available on the COVID-19 Wider Impact Dashboard. Novel coronavirus (COVID-19) is a new strain of coronavirus first identified in Wuhan, China. Clinical presentation may range from mild-to-moderate illness to pneumonia or severe acute respiratory infection. COVID-19 was declared a pandemic by the World Health Organisation on 12 March 2020. We now have spread of COVID-19 within communities in the UK. Public Health Scotland no longer reports the number of COVID-19 deaths within 28 days of a first positive test from 2nd June 2022. Please refer to NRS death certificate data as the single source for COVID-19 deaths data in Scotland. In the process of updating the hospital admissions reporting to include reinfections, we have had to review existing methodology. In order to provide the best possible linkage of COVID-19 cases to hospital admissions, each admission record is required to have a discharge date, to allow us to better match the most appropriate COVID positive episode details to an admission. This means that in cases where the discharge date is missing (either due to the patient still being treated, delays in discharge information being submitted or data quality issues), it has to be estimated. Estimating a discharge date for historic records means that the average stay for those with missing dates is reduced, and fewer stays overlap with records of positive tests. The result of these changes has meant that approximately 1,200 historic COVID admissions have been removed due to improvements in methodology to handle missing discharge dates, while approximately 820 have been added to the cumulative total with the inclusion of reinfections. COVID-19 hospital admissions are now identified as the following: A patient's first positive PCR or LFD test of the episode of infection (including reinfections at 90 days or more) for COVID-19 up to 14 days prior to admission to hospital, on the day of their admission or during their stay in hospital. If a patient's first positive PCR or LFD test of the episode of infection is after their date of discharge from hospital, they are not included in the analysis. Information on COVID-19, including stay at home advice for people who are self-isolating and their households, can be found on NHS Inform. Data visualisation of Scottish COVID-19 cases is available on the Public Health Scotland - Covid 19 Scotland dashboard. Further information on coronavirus in Scotland is available on the Scottish Government - Coronavirus in Scotland page, where further breakdown of past coronavirus data has also been published.

  19. MDCOVID19 ConfirmedDeathsByGenderDistribution

    • hub.arcgis.com
    • data.imap.maryland.gov
    • +3more
    Updated May 22, 2020
    + more versions
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    ArcGIS Online for Maryland (2020). MDCOVID19 ConfirmedDeathsByGenderDistribution [Dataset]. https://hub.arcgis.com/maps/maryland::mdcovid19-confirmeddeathsbygenderdistribution/about
    Explore at:
    Dataset updated
    May 22, 2020
    Dataset provided by
    https://arcgis.com/
    Authors
    ArcGIS Online for Maryland
    Description

    SummaryThe cumulative number of confirmed COVID-19 deaths among Maryland residents by gender: Female; Male; Unknown.DescriptionThe MD COVID-19 - Confirmed Deaths by Gender Distribution data layer is a collection of the statewide confirmed and probable COVID-19 related deaths that have been reported each day by the Vital Statistics Administration by gender. A death is classified as confirmed if the person had a laboratory-confirmed positive COVID-19 test result. Some data on deaths may be unavailable due to the time lag between the death, typically reported by a hospital or other facility, and the submission of the complete death certificate. Probable deaths are available from the MD COVID-19 - Probable Deaths by Gender Distribution data layer.COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county.

  20. m

    MDCOVID19 TotalProbableDeathsStatewide

    • data.imap.maryland.gov
    • hub.arcgis.com
    • +1more
    Updated May 22, 2020
    + more versions
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    ArcGIS Online for Maryland (2020). MDCOVID19 TotalProbableDeathsStatewide [Dataset]. https://data.imap.maryland.gov/datasets/mdcovid19-totalprobabledeathsstatewide
    Explore at:
    Dataset updated
    May 22, 2020
    Dataset authored and provided by
    ArcGIS Online for Maryland
    Description

    SummaryThe cumulative number of probable COVID-19 deaths among Maryland residents.DescriptionThe MD COVID-19 - Total Probable Deaths Statewide data layer is a collection of the statewide probable COVID-19 related deaths that have been reported each day by the Vital Statistics Administration. A death is classified as probable if the person's death certificate notes COVID-19 to be a probable, suspect or presumed cause or condition. Probable deaths are not yet been confirmed by a laboratory test. Some data on deaths may be unavailable due to the time lag between the death, typically reported by a hospital or other facility, and the submission of the complete death certificate. Confirmed deaths are available from the MD COVID-19 - Total Confirmed Deaths Statewide data layer.COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county.

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TRADING ECONOMICS (2022). China Coronavirus COVID-19 Cases [Dataset]. https://tradingeconomics.com/china/coronavirus-cases

China Coronavirus COVID-19 Cases

China Coronavirus COVID-19 Cases - Historical Dataset (2020-01-04/2023-05-17)

Explore at:
excel, csv, xml, jsonAvailable download formats
Dataset updated
May 29, 2022
Dataset authored and provided by
TRADING ECONOMICS
License

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

Time period covered
Jan 4, 2020 - May 17, 2023
Area covered
China
Description

China recorded 99256991 Coronavirus Cases since the epidemic began, according to the World Health Organization (WHO). In addition, China reported 5226 Coronavirus Deaths. This dataset includes a chart with historical data for China Coronavirus Cases.

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