100+ datasets found
  1. Confirmed, death and recovery cases of COVID-19 in Greater China 2022, by...

    • statista.com
    Updated Sep 2, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2024). Confirmed, death and recovery cases of COVID-19 in Greater China 2022, by region [Dataset]. https://www.statista.com/statistics/1090007/china-confirmed-and-suspected-wuhan-coronavirus-cases-region/
    Explore at:
    Dataset updated
    Sep 2, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    China
    Description

    The new SARS-like coronavirus has spread around China since its outbreak in Wuhan - the capital of central China’s Hubei province. As of June 7, 2022, there were 2,785,848 active cases with symptoms in Greater China. The pandemic has caused a significant impact in the country's economy.

    Fast-moving epidemic

    In Wuhan, over 3.8 thousand deaths were registered in the heart of the outbreak. The total infection number surged on February 12, 2020 in Hubei province. After a change in official methodology for diagnosing and counting cases, thousands of new cases were added to the total figure. There is little knowledge about how the virus that originated from animals transferred to humans. While human-to-human transmission has been confirmed, other transmission routes through aerosol and fecal-oral are also possible. The deaths from the current virus COVID-19 (formally known as 2019-nCoV) has surpassed the toll from the SARS epidemic of 2002 and 2003.

    Key moments in the Chinese coronavirus timeline

    The doctor in Wuhan, Dr. Li Wenliang, who first warned about the new strain of coronavirus was silenced by the police. It was announced on February 7, 2020 that he died from the effects of the coronavirus infection. His death triggered a national backlash over freedom of speech on Chinese social media. On March 18, 2020, the Chinese government reported no new domestically transmissions for the first time after a series of quarantine and social distancing measures had been implemented. On March 31, 2020, the National Health Commission (NHC) in China started reporting the infection number of symptom-free individuals who tested positive for coronavirus. Before that, asymptomatic cases had not been included in the Chinese official count. China lifted ten-week lockdown on Wuhan on April 8, 2020. Daily life was returning slowly back to normal in the country. On April 17, 2020, health authorities in Wuhan revised its death toll, adding some 1,290 fatalities in its total count.

  2. COVID-19 confirmed and death case development in China 2020-2022

    • statista.com
    Updated Mar 20, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    COVID-19 confirmed and death case development in China 2020-2022 [Dataset]. https://www.statista.com/statistics/1092918/china-wuhan-coronavirus-2019ncov-confirmed-and-deceased-number/
    Explore at:
    Dataset updated
    Mar 20, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 20, 2020 - Jun 6, 2022
    Area covered
    China
    Description

    As of June 6, 2022, the novel coronavirus SARS-CoV-2 that originated in Wuhan, the capital of Hubei province in China, had infected over 2.1 million people and killed 14,612 in the country. Hong Kong is currently the region with the highest active cases in China.

    From Wuhan to the rest of China

    In late December 2019, health authorities in Wuhan detected several pneumonia cases of unknown cause. Most of these patients had links to the Huanan Seafood Market. With Chinese New Year approaching, millions of Chinese migrant workers travelled back to their hometowns for the celebration. Before the start of the travel ban on January 23, around five million people had left Wuhan. By the end of January, the number of infections had surged to over ten thousand. The death toll from the virus exceeded that of the SARS outbreak a few days later. On February 12, thousands more cases were confirmed in Wuhan after an improvement to the diagnosis method, resulting in another sudden surge of confirmed cases. On March 31, 2020, the National Health Commission (NHC) in China announced that it would begin reporting the infection number of symptom-free individuals who tested positive for coronavirus. On April 17, 2020, health authorities in Wuhan revised its death toll, adding 50 percent more fatalities. After quarantine measures were implemented, the country reported no new local coronavirus COVID-19 transmissions for the first time on March 18, 2020.

    The overloaded healthcare system

    In Wuhan, 28 hospitals were designated to treat coronavirus patients, but the outbreak continued to test China’s disease control system and most of the hospitals were soon fully occupied. To combat the virus, the government announced plans to build a new hospital swiftly. On February 3, 2020, Huoshenshan Hospital was opened to provide an additional 1,300 beds. Due to an extreme shortage of health-care professionals in Wuhan, thousands of medical staff from all over China came voluntarily to the epicenter to offer their support. After no new deaths reported for first time, China lifted ten-week lockdown on Wuhan on April 8, 2020. Daily life was returning slowly back to normal in the country.

  3. C

    China CN: COVID-19: Confirmed Case: New Increase

    • ceicdata.com
    Updated Dec 15, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    China CN: COVID-19: Confirmed Case: New Increase [Dataset]. https://www.ceicdata.com/en/china/covid19-no-of-patient/cn-covid19-confirmed-case-new-increase
    Explore at:
    Dataset updated
    Dec 15, 2024
    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
    Apr 29, 2020 - May 10, 2020
    Area covered
    China
    Description

    China COVID-19: Confirmed Case: New Increase data was reported at 17.000 Person in 10 May 2020. This records an increase from the previous number of 14.000 Person for 09 May 2020. China COVID-19: Confirmed Case: New Increase data is updated daily, averaging 51.000 Person from Jan 2020 (Median) to 10 May 2020, with 112 observations. The data reached an all-time high of 15,152.000 Person in 12 Feb 2020 and a record low of 1.000 Person in 08 May 2020. China COVID-19: Confirmed Case: New Increase data remains active status in CEIC and is reported by National Health Commission. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GZ: COVID-19: No of Patient.

  4. n

    Coronavirus COVID-19 Cases V2

    • prep-response-portal.napsgfoundation.org
    • gis-for-secondary-schools-schools-be.hub.arcgis.com
    • +1more
    Updated Mar 26, 2020
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    CSSE_covid19 (2020). Coronavirus COVID-19 Cases V2 [Dataset]. https://prep-response-portal.napsgfoundation.org/maps/1cb306b5331945548745a5ccd290188e
    Explore at:
    Dataset updated
    Mar 26, 2020
    Dataset authored and provided by
    CSSE_covid19
    Area covered
    Pacific Ocean, North Pacific Ocean
    Description

    On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit the following sources:Global: World Health Organization (WHO)U.S.: U.S. Centers for Disease Control and Prevention (CDC)For more information, visit the Johns Hopkins Coronavirus Resource Center.This feature layer contains the most up-to-date COVID-19 cases and latest trend plot. It covers China, Canada, Australia (at province/state level), and the rest of the world (at country level, represented by either the country centroids or their capitals)and the US at county-level. Data sources: WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, BNO, state and national government health departments, and local media reports. . The China data is automatically updating at least once per hour, and non-China data is updating hourly. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. This feature layer is supported by Esri Living Atlas team and JHU Data Services. This layer is opened to the public and free to share. Contact us.

  5. T

    China Coronavirus COVID-19 Cases

    • tradingeconomics.com
    csv, excel, json, xml
    Updated May 29, 2022
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    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.

  6. COVID-19 case death rate trend in China 2020-2023

    • statista.com
    Updated Mar 20, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2023). COVID-19 case death rate trend in China 2020-2023 [Dataset]. https://www.statista.com/statistics/1108866/china-novel-coronavirus-covid19-case-fatality-rate-development/
    Explore at:
    Dataset updated
    Mar 20, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 19, 2020 - Jan 1, 2023
    Area covered
    China
    Description

    As of January 1, 2023, the case fatality rate (CFR) of coronavirus COVID-19 ranged at 0.27 percent in China, lower than the global level of 1.01 percent. Health authorities in Wuhan, the Chinese epicenter, revised its death toll on April 17, adding some 1,290 fatalities to its total count. The 50 percent increase of death cases in the city raised the overall CFR in China from 4.06 percent to 5.6 percent. The Chinese Center for Disease Control and Prevention reported that mortality increased with age among infected patients.

  7. H

    Novel Coronavirus (COVID-19) Cases Data

    • data.humdata.org
    csv
    Updated Feb 4, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Johns Hopkins University Center for Systems Science and Engineering (2025). Novel Coronavirus (COVID-19) Cases Data [Dataset]. https://data.humdata.org/dataset/novel-coronavirus-2019-ncov-cases
    Explore at:
    csvAvailable download formats
    Dataset updated
    Feb 4, 2025
    Dataset provided by
    Johns Hopkins University Center for Systems Science and Engineering
    License

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

    Description
    JHU Has Stopped Collecting Data As Of 03/10/2023
    After three years of around-the-clock tracking of COVID-19 data from around the world, Johns Hopkins has discontinued the Coronavirus Resource Center’s operations.
    The site’s two raw data repositories will remain accessible for information collected from 1/22/20 to 3/10/23 on cases, deaths, vaccines, testing and demographics.

    Novel Corona Virus (COVID-19) epidemiological data since 22 January 2020. The data is compiled by the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) from various sources including the World Health Organization (WHO), DXY.cn, BNO News, National Health Commission of the People’s Republic of China (NHC), China CDC (CCDC), Hong Kong Department of Health, Macau Government, Taiwan CDC, US CDC, Government of Canada, Australia Government Department of Health, European Centre for Disease Prevention and Control (ECDC), Ministry of Health Singapore (MOH), and others. JHU CCSE maintains the data on the 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository on Github.

    Fields available in the data include Province/State, Country/Region, Last Update, Confirmed, Suspected, Recovered, Deaths.

    On 23/03/2020, a new data structure was released. The current resources for the latest time series data are:

    • time_series_covid19_confirmed_global.csv
    • time_series_covid19_deaths_global.csv
    • time_series_covid19_recovered_global.csv

    ---DEPRECATION WARNING---
    The resources below ceased being updated on 22/03/2020 and were removed on 26/03/2020:

    • time_series_19-covid-Confirmed.csv
    • time_series_19-covid-Deaths.csv
    • time_series_19-covid-Recovered.csv
  8. 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/dataset/covid-19-trends-in-each-country
    Explore at:
    esri rest, htmlAvailable 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

  9. Cases

    • data.amerigeoss.org
    csv, esri rest +4
    Updated Aug 11, 2020
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    ESRI (2020). Cases [Dataset]. https://data.amerigeoss.org/mk/dataset/cases2
    Explore at:
    html, kml, esri rest, geojson, zip, csvAvailable download formats
    Dataset updated
    Aug 11, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Description
    This feature layer contains the most up-to-date COVID-19 cases and latest trend plot. It covers China, Canada, Australia (at province/state level), and the rest of the world (at country level, represented by either the country centroids or their capitals)and the US at county-level. Data sources: WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, BNO, state and national government health departments, and local media reports. . The China data is automatically updating at least once per hour, and non-China data is updating hourly. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. This feature layer is supported by Esri Living Atlas team and JHU Data Services. This layer is opened to the public and free to share. Contact us.
  10. f

    Table_1_Estimation of Local Novel Coronavirus (COVID-19) Cases in Wuhan,...

    • figshare.com
    • frontiersin.figshare.com
    docx
    Updated Jun 1, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Zian Zhuang; Peihua Cao; Shi Zhao; Yijun Lou; Shu Yang; Weiming Wang; Lin Yang; Daihai He (2023). Table_1_Estimation of Local Novel Coronavirus (COVID-19) Cases in Wuhan, China from Off-Site Reported Cases and Population Flow Data from Different Sources.docx [Dataset]. http://doi.org/10.3389/fphy.2020.00336.s001
    Explore at:
    docxAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    Frontiers
    Authors
    Zian Zhuang; Peihua Cao; Shi Zhao; Yijun Lou; Shu Yang; Weiming Wang; Lin Yang; Daihai He
    License

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

    Area covered
    Wuhan, China
    Description

    In December 2019, novel coronavirus disease (COVID-19) hit Wuhan, Hubei Province, China and spread to the rest of China and overseas. The emergence of this virus coincided with the Spring Festival Travel Rush in China. It is possible to estimate the total number of COVID-19 cases in Wuhan, by 23 January 2020, given the cases reported in other cities/regions and population flow data between Wuhan and these cities/regions. We built a model to estimate the total number of COVID-19 cases in Wuhan by 23 January 2020, based on the number of cases detected outside Wuhan city in China, with the assumption that cases exported from Wuhan were less likely underreported in other cities/regions. We employed population flow data from different sources between Wuhan and other cities/regions by 23 January 2020. The number of total cases in Wuhan was determined by the maximum log likelihood estimation and Akaike Information Criterion (AIC) weight. We estimated 8 679 (95% CI: 7 701, 9 732) as total COVID-19 cases in Wuhan by 23 January 2020, based on combined source of data from Tencent and Baidu. Sources of population flow data impact the estimates of the total number of COVID-19 cases in Wuhan before city lockdown. We should make a comprehensive analysis based on different sources of data to overcome the bias from different sources.

  11. Age distribution of COVID-19 patients in China 2020

    • statista.com
    Updated Aug 28, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2024). Age distribution of COVID-19 patients in China 2020 [Dataset]. https://www.statista.com/statistics/1095024/china-age-distribution-of-wuhan-coronavirus-covid-19-patients/
    Explore at:
    Dataset updated
    Aug 28, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    China
    Description

    According to a medical analysis based on 44,672 confirmed cases of the novel coronavirus COVID-19 in China published in February 2020, most patients aged between 30 and 69 years. Approximately ten percent of the surveyed patients were 29 years old or younger. The same report revealed that mortality increased with age among Chinese COVID-19 cases. The most common symptoms were fever and dry cough.

  12. Coronavirus COVID-19 Global Cases

    • redivis.com
    application/jsonl +7
    Updated Jul 13, 2020
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Stanford Center for Population Health Sciences (2020). Coronavirus COVID-19 Global Cases [Dataset]. http://doi.org/10.57761/pyf5-4e40
    Explore at:
    application/jsonl, parquet, csv, stata, avro, spss, sas, arrowAvailable 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/

  13. C

    China CN: COVID-19: Confirmed Case: Local: Hospitalized: Critical Case:...

    • ceicdata.com
    Updated Dec 15, 2024
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    China CN: COVID-19: Confirmed Case: Local: Hospitalized: Critical Case: Shanghai [Dataset]. https://www.ceicdata.com/en/china/covid19-no-of-patient-local/cn-covid19-confirmed-case-local-hospitalized-critical-case-shanghai
    Explore at:
    Dataset updated
    Dec 15, 2024
    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
    Nov 26, 2022 - Dec 7, 2022
    Area covered
    China
    Description

    COVID-19: Confirmed Case: Local: Hospitalized: Critical Case: Shanghai data was reported at 0.000 Person in 07 Dec 2022. This stayed constant from the previous number of 0.000 Person for 06 Dec 2022. COVID-19: Confirmed Case: Local: Hospitalized: Critical Case: Shanghai data is updated daily, averaging 0.000 Person from Apr 2022 (Median) to 07 Dec 2022, with 232 observations. The data reached an all-time high of 99.000 Person in 04 May 2022 and a record low of 0.000 Person in 07 Dec 2022. COVID-19: Confirmed Case: Local: Hospitalized: Critical Case: Shanghai data remains active status in CEIC and is reported by Shanghai Municipal Health Commission. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GZ: COVID-19: No of Patient: Local.

  14. JHU COVID-19 Cases

    • mea-covid-19-esridubaioffice.hub.arcgis.com
    Updated Apr 2, 2020
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Esri Inc. Office in Dubai (2020). JHU COVID-19 Cases [Dataset]. https://mea-covid-19-esridubaioffice.hub.arcgis.com/datasets/jhu-covid-19-cases
    Explore at:
    Dataset updated
    Apr 2, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri Inc. Office in Dubai
    Area covered
    Pacific Ocean, North Pacific Ocean
    Description

    This feature layer contains the most up-to-date COVID-19 cases and latest trend plot. It covers China, the US, Canada, Australia (at province/state level), and the rest of the world (at country level, represented by either the country centroids or their capitals). Data sources are WHO, US CDC, China NHC, ECDC, and DXY. The China data is automatically updating at least once per hour, and non China data is updating manually. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. This feature layer is supported by Esri Living Atlas team and JHU Data Services. This layer is opened to the public and free to share. Contact us.

  15. COVID-19 Trends in Each Country

    • coronavirus-resources.esri.com
    • coronavirus-response-israel-systematics.hub.arcgis.com
    • +2more
    Updated Mar 27, 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://coronavirus-resources.esri.com/maps/a16bb8b137ba4d8bbe645301b80e5740
    Explore at:
    Dataset updated
    Mar 27, 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

  16. f

    Data from: Modeling outbreaks of COVID-19 in China: The impact of...

    • tandf.figshare.com
    tiff
    Updated Feb 11, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Wenting Zha; Han Ni; Yuxi He; Wentao Kuang; Jin Zhao; Liuyi Fu; Haoyun Dai; Yuan Lv; Nan Zhou; Xuewen Yang (2025). Modeling outbreaks of COVID-19 in China: The impact of vaccination and other control measures on curbing the epidemic [Dataset]. http://doi.org/10.6084/m9.figshare.25687165.v1
    Explore at:
    tiffAvailable download formats
    Dataset updated
    Feb 11, 2025
    Dataset provided by
    Taylor & Francis
    Authors
    Wenting Zha; Han Ni; Yuxi He; Wentao Kuang; Jin Zhao; Liuyi Fu; Haoyun Dai; Yuan Lv; Nan Zhou; Xuewen Yang
    License

    Attribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/
    License information was derived automatically

    Area covered
    China
    Description

    This study aims to examine the development trend of COVID-19 in China and propose a model to assess the impacts of various prevention and control measures in combating the COVID-19 pandemic. Using COVID-19 cases reported by the National Health Commission of China from January 2, 2020, to January 2, 2022, we established a Susceptible-Exposed-Infected-Asymptomatic-Quarantined-Vaccinated-Hospitalized-Removed (SEIAQVHR) model to calculate the COVID-19 transmission rate and Rt effective reproduction number, and assess prevention and control measures. Additionally, we built a stochastic model to explore the development of the COVID-19 epidemic. We modeled the incidence trends in five outbreaks between 2020 and 2022. Some important features of the COVID-19 epidemic are mirrored in the estimates based on our SEIAQVHR model. Our model indicates that an infected index case entering the community has a 50%–60% chance to cause a COVID-19 outbreak. Wearing masks and getting vaccinated were the most effective measures among all the prevention and control measures. Specifically targeting asymptomatic individuals had no significant impact on the spread of COVID-19. By adjusting prevention and control parameters, we suggest that increasing the rates of effective vaccination and mask-wearing can significantly reduce COVID-19 cases in China. Our stochastic model analysis provides a useful tool for understanding the COVID-19 epidemic in China.

  17. All Visited and Resided buildings of probable or confirmed COVID-19 cases in...

    • opendata.esrichina.hk
    • hub.arcgis.com
    Updated Oct 26, 2021
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Esri China (Hong Kong) Ltd. (2021). All Visited and Resided buildings of probable or confirmed COVID-19 cases in Hong Kong [Dataset]. https://opendata.esrichina.hk/maps/28780fc84ca4472796d693cd602bb043
    Explore at:
    Dataset updated
    Oct 26, 2021
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri China (Hong Kong) Ltd.
    Area covered
    Description

    This layer shows the location of all visited and resided buildings of probable or confirmed COVID-19 cases in Hong Kong. It is a set of data made available by the Department of Health under the Government of Hong Kong Special Administrative Region (the "Government") at https://GEODATA.GOV.HK/ ("Hong Kong Geodata Store"). The source data is in GML format and has been processed and converted into Esri File Geodatabase format and uploaded to Esri's ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of Hong Kong Geodata Store at https://geodata.gov.hk/.

  18. Age comparison of COVID-19 fatality rate in China 2020

    • statista.com
    Updated Aug 28, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2024). Age comparison of COVID-19 fatality rate in China 2020 [Dataset]. https://www.statista.com/statistics/1099662/china-wuhan-coronavirus-covid-19-fatality-rate-by-age-group/
    Explore at:
    Dataset updated
    Aug 28, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    China
    Description

    According to a medical analysis of 44,672 confirmed COVID-19 cases in China, the overall fatality rate of the novel coronavirus was 2.3 percent. As of February 11, 2020, the fatality rate of patients aged 80 years and older was 14.8 percent.

  19. Characteristics of 39 COVID-19 cases in Guangzhou, China.

    • plos.figshare.com
    xls
    Updated May 31, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Lei Luo; Dan Liu; Hao Zhang; Zhihao Li; Ruonan Zhen; Xiru Zhang; Huaping Xie; Weiqi Song; Jie Liu; Qingmei Huang; Jingwen Liu; Xingfen Yang; Zongqiu Chen; Chen Mao (2023). Characteristics of 39 COVID-19 cases in Guangzhou, China. [Dataset]. http://doi.org/10.1371/journal.pntd.0008570.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Lei Luo; Dan Liu; Hao Zhang; Zhihao Li; Ruonan Zhen; Xiru Zhang; Huaping Xie; Weiqi Song; Jie Liu; Qingmei Huang; Jingwen Liu; Xingfen Yang; Zongqiu Chen; Chen Mao
    License

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

    Area covered
    Guangzhou, China
    Description

    Characteristics of 39 COVID-19 cases in Guangzhou, China.

  20. cumulative numbers of COVID-19 infections in China (excluding imported...

    • figshare.com
    xls
    Updated Aug 24, 2022
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Yang Liu (2022). cumulative numbers of COVID-19 infections in China (excluding imported cases) from January 20 to March 15, 2020 [Dataset]. http://doi.org/10.6084/m9.figshare.20604336.v1
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Aug 24, 2022
    Dataset provided by
    figshare
    Authors
    Yang Liu
    License

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

    Area covered
    China
    Description

    cumulative numbers of COVID-19 infections in China (excluding imported cases) from January 20 to March 15, 2020

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Statista (2024). Confirmed, death and recovery cases of COVID-19 in Greater China 2022, by region [Dataset]. https://www.statista.com/statistics/1090007/china-confirmed-and-suspected-wuhan-coronavirus-cases-region/
Organization logo

Confirmed, death and recovery cases of COVID-19 in Greater China 2022, by region

Explore at:
7 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Sep 2, 2024
Dataset authored and provided by
Statistahttp://statista.com/
Area covered
China
Description

The new SARS-like coronavirus has spread around China since its outbreak in Wuhan - the capital of central China’s Hubei province. As of June 7, 2022, there were 2,785,848 active cases with symptoms in Greater China. The pandemic has caused a significant impact in the country's economy.

Fast-moving epidemic

In Wuhan, over 3.8 thousand deaths were registered in the heart of the outbreak. The total infection number surged on February 12, 2020 in Hubei province. After a change in official methodology for diagnosing and counting cases, thousands of new cases were added to the total figure. There is little knowledge about how the virus that originated from animals transferred to humans. While human-to-human transmission has been confirmed, other transmission routes through aerosol and fecal-oral are also possible. The deaths from the current virus COVID-19 (formally known as 2019-nCoV) has surpassed the toll from the SARS epidemic of 2002 and 2003.

Key moments in the Chinese coronavirus timeline

The doctor in Wuhan, Dr. Li Wenliang, who first warned about the new strain of coronavirus was silenced by the police. It was announced on February 7, 2020 that he died from the effects of the coronavirus infection. His death triggered a national backlash over freedom of speech on Chinese social media. On March 18, 2020, the Chinese government reported no new domestically transmissions for the first time after a series of quarantine and social distancing measures had been implemented. On March 31, 2020, the National Health Commission (NHC) in China started reporting the infection number of symptom-free individuals who tested positive for coronavirus. Before that, asymptomatic cases had not been included in the Chinese official count. China lifted ten-week lockdown on Wuhan on April 8, 2020. Daily life was returning slowly back to normal in the country. On April 17, 2020, health authorities in Wuhan revised its death toll, adding some 1,290 fatalities in its total count.

Search
Clear search
Close search
Google apps
Main menu