Refine your search: Clear all
Featured:
Locations:
More
Formats:
More
Organisations:
More
Tags:
More
Licenses:
More
  • 5400+ Downloads
    Updated Live | Dataset date: December 01, 2019-June 18, 2021
    This dataset updates: Live
    Data Overview This repository contains spatiotemporal data from many official sources for 2019-Novel Coronavirus beginning 2019 in Hubei, China ("nCoV_2019") You may not use this data for commercial purposes. If there is a need for commercial use of the data, please contact Metabiota at info@metabiota.com to obtain a commercial use license. The incidence data are in a CSV file format. One row in an incidence file contains a piece of epidemiological data extracted from the specified source. The file contains data from multiple sources at multiple spatial resolutions in cumulative and non-cumulative formats by confirmation status. To select a single time series of case or death data, filter the incidence dataset by source, spatial resolution, location, confirmation status, and cumulative flag. Data are collected, structured, and validated by Metabiota’s digital surveillance experts. The data structuring process is designed to produce the most reliable estimates of reported cases and deaths over space and time. The data are cleaned and provided in a uniform format such that information can be compared across multiple sources. Data are collected at the time of publication in the highest geographic and temporal resolutions available in the original report. This repository is intended to provide a single access point for data from a wide range of data sources. Data will be updated periodically with the latest epidemiological data. Metabiota maintains a database of epidemiological information for over two thousand high-priority infectious disease events. Please contact us (info@metabiota.com) if you are interested in licensing the complete dataset. Cumulative vs. Non-Cumulative Incidence Reporting sources provide either cumulative incidence, non-cumulative incidence, or both. If the source only provides a non-cumulative incidence value, the cumulative values are inferred using prior reports from the same source. Use the CUMULATIVE FLAG variable to subset the data to cumulative (TRUE) or non-cumulative (FALSE) values. Case Confirmation Status The incidence datasets include the confirmation status of cases and deaths when this information is provided by the reporting source. Subset the data by the CONFIRMATION_STATUS variable to either TOTAL, CONFIRMED, SUSPECTED, or PROBABLE to obtain the data of your choice. Total incidence values include confirmed, suspected, and probable incidence values. If a source only provides suspected, probable, or confirmed incidence, the total incidence is inferred to be the sum of the provided values. If the report does not specify confirmation status, the value is included in the "total" confirmation status value. The data provided under the "Metabiota Composite Source" often does not include suspected incidence due to inconsistencies in reporting cases and deaths with this confirmation status. Outcome - Cases vs. Deaths The incidence datasets include cases and deaths. Subset the data to either CASE or DEATH using the OUTCOME variable. It should be noted that deaths are included in case counts. Spatial Resolution Data are provided at multiple spatial resolutions. Data should be subset to a single spatial resolution of interest using the SPATIAL_RESOLUTION variable. Information is included at the finest spatial resolution provided to the original epidemic report. We also aggregate incidence to coarser geographic resolutions. For example, if a source only provides data at the province-level, then province-level data are included in the dataset as well as country-level totals. Users should avoid summing all cases or deaths in a given country for a given date without specifying the SPATIAL_RESOLUTION value. For example, subset the data to SPATIAL_RESOLUTION equal to “AL0” in order to view only the aggregated country level data. There are differences in administrative division naming practices by country. Administrative levels in this dataset are defined using the Google Geolocation API (https://developers.google.com/maps/documentation/geolocation/). For example, the data for the 2019-nCoV from one source provides information for the city of Beijing, which Google Geolocations indicates is a “locality.” Beijing is also the name of the municipality where the city Beijing is located. Thus, the 2019-nCoV dataset includes rows of data for both the city Beijing, as well as the municipality of the same name. If additional cities in the Beijing municipality reported data, those data would be aggregated with the city Beijing data to form the municipality Beijing data. Sources Data sources in this repository were selected to provide comprehensive spatiotemporal data for each outbreak. Data from a specific source can be selected using the SOURCE variable. In addition to the original reporting sources, Metabiota compiles multiple sources to generate the most comprehensive view of an outbreak. This compilation is stored in the database under the source name “Metabiota Composite Source.” The purpose of generating this new view of the outbreak is to provide the most accurate and precise spatiotemporal data for the outbreak. At this time, Metabiota does not incorporate unofficial - including media - sources into the “Metabiota Composite Source” dataset. Quality Assurance Data are collected by a team of digital surveillance experts and undergo many quality assurance tests. After data are collected, they are independently verified by at least one additional analyst. The data also pass an automated validation program to ensure data consistency and integrity. NonCommercial Use License Creative Commons License Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) This is a human-readable summary of the Legal Code. You are free: to Share — to copy, distribute and transmit the work to Remix — to adapt the work Under the following conditions: Attribution — You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work). Noncommercial — You may not use this work for commercial purposes. Share Alike — If you alter, transform, or build upon this work, you may distribute the resulting work only under the same or similar license to this one. With the understanding that: Waiver — Any of the above conditions can be waived if you get permission from the copyright holder. Public Domain — Where the work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license. Other Rights — In no way are any of the following rights affected by the license: Your fair dealing or fair use rights, or other applicable copyright exceptions and limitations; The author's moral rights; Rights other persons may have either in the work itself or in how the work is used, such as publicity or privacy rights. Notice — For any reuse or distribution, you must make clear to others the license terms of this work. The best way to do this is with a link to this web page. For details and the full license text, see http://creativecommons.org/licenses/by-nc-sa/3.0/ Liability Metabiota shall in no event be liable for any decision taken by the user based on the data made available. Under no circumstances, shall Metabiota be liable for any damages (whatsoever) arising out of the use or inability to use the database. The entire risk arising out of the use of the database remains with the user.
  • 1200+ Downloads
    Updated 19 June 2021 | Dataset date: November 30, 2020-May 24, 2021
    This dataset updates: Every month
    Understanding gender is essential to understanding the risk factors of poor health, early death and health inequities. The COVID-19 outbreak is no different. At this point in the pandemic, we are unable to provide a clear answer to the question of the extent to which sex and gender are influencing the health outcomes of people diagnosed with COVID-19. However, experience and evidence thus far tell us that both sex and gender are important drivers of risk and response to infection and disease. In order to understand the role gender is playing in the COVID-19 outbreak, countries urgently need to begin both collecting and publicly reporting sex-disaggregated data. At a minimum, this should include the number of cases and deaths in men and women. In collaboration with CNN, Global Health 50/50 began compiling publicly available sex-disaggregated data reported by national governments to date and is exploring how gender may be driving the higher proportion of reported deaths in men among confirmed cases so far. For more, please visit: http://globalhealth5050.org/covid19
  • 800+ Downloads
    Updated 19 June 2021 | Dataset date: January 01, 2019-June 19, 2021
    This dataset updates: Every day
    This dataset contains the number of confirmed cases, recoveries and deaths by province due to the Coronavirus pandemic in Afghanistan.
  • 7400+ Downloads
    Updated 19 June 2021 | Dataset date: March 10, 2020-May 27, 2021
    This dataset updates: Every day
    This data has been collected from various sources and is displayed in this online dashboard: https://geonode.wfp.org/travel Mobile version: https://geonode.wfp.org/travel_mobile The data is divided in two datasets: COVID-19 restrictions by country: This dataset shows current travel restrictions. Information is collected from various sources: IATA, media, national sources, WFP internal or any other. COVID-19 airline restrictions information: This dataset shows restrictions taken by individual airlines or country. Information is collected again from various sources including WFP internal and public sources. The data displayed is a collaborative effort and anybody with more accurate/updated information is highly encouraged to contact WFP GIS unit for Emergencies at the following email address: hq.gis@wfp.org
  • Updated 18 June 2021 | Dataset date: June 18, 2021-June 19, 2021
    This dataset updates: As needed
    Somalia location of health facilities by region and district.
  • 100+ Downloads
    Updated 18 June 2021 | Dataset date: November 15, 2020-June 19, 2021
    This dataset updates: Every day
    Contains data crowdsourced from Venezuelans through the Premise Data mobile application. The survey is presented only once to users and aims to capture current COVID-19 awareness around testing availability and symptoms, and identifies users who have moved to a different state in the last year. More relevant information below: The booklet included HERE goes into more details on how Premise's crowdsourcing works.
  • 100+ Downloads
    Updated 18 June 2021 | Dataset date: June 16, 2020-June 19, 2021
    This dataset updates: Every day
    Contains data crowdsourced monthly from Venezuelans through the Premise Data mobile application. The data collected tracks access to health care services and general market access to essential and chronic non-transmittable medicine. More relevant information below: The booklet included HERE goes into more details on how Premise's crowdsourcing works.
  • 200+ Downloads
    Updated 18 June 2021 | Dataset date: April 01, 2021-June 19, 2021
    This dataset updates: Every day
    This dataset contains the number of confirmed cases, recoveries and deaths by Governorate due to the Coronavirus pandemic in Palestine.
  • 6300+ Downloads
    Updated 18 June 2021 | Dataset date: January 01, 1960-December 31, 2019
    This dataset updates: As needed
    World Bank Indicators of Interest to the COVID-19 Outbreak. This link is to a collection in the World Bank data catalog that contains datasets that may be useful for analysis, response or modelling.
  • 4900+ Downloads
    Updated 17 June 2021 | Dataset date: March 02, 2020-June 16, 2021
    This dataset updates: Every day
    This dataset contains the number of confirmed cases, recoveries and deaths by province due to the Coronavirus pandemic in Indonesia.
  • 7700+ Downloads
    Updated 17 June 2021 | Dataset date: December 16, 2020-June 16, 2021
    This dataset updates: Every day
    The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak. Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak. We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak. The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository. United States Data Data on cumulative coronavirus cases and deaths can be found in two files for states and counties. Each row of data reports cumulative counts based on our best reporting up to the moment we publish an update. We do our best to revise earlier entries in the data when we receive new information. Both files contain FIPS codes, a standard geographic identifier, to make it easier for an analyst to combine this data with other data sets like a map file or population data. State-Level Data State-level data can be found in the us-states.csv file. date,state,fips,cases,deaths 2020-01-21,Washington,53,1,0 ... County-Level Data County-level data can be found in the us-counties.csv file. date,county,state,fips,cases,deaths 2020-01-21,Snohomish,Washington,53061,1,0 ... In some cases, the geographies where cases are reported do not map to standard county boundaries. See the list of geographic exceptions for more detail on these. Github Repository This dataset contains COVID-19 data for the United States of America made available by The New York Times on github at https://github.com/nytimes/covid-19-data
  • 100+ Downloads
    Updated 17 June 2021 | Dataset date: January 16, 2021-June 15, 2021
    This dataset updates: Every week
    Covid19 data at the city level in Mauritania - Infections (new cases), Deaths, Recoveries.
  • 500+ Downloads
    Updated 17 June 2021 | Dataset date: March 15, 2020-June 15, 2021
    This dataset updates: Every week
    Covid-19 data at the city level in Mali - Infections (new cases, gender), Deaths, Recoveries
  • 800+ Downloads
    Updated 17 June 2021 | Dataset date: March 09, 2020-June 14, 2021
    This dataset updates: Every week
    Covid-19 data at the city level in Burkina Faso - Infections (new cases, gender), Deaths, Recoveries + Urban / Rural locations.
  • 700+ Downloads
    Updated 17 June 2021 | Dataset date: March 13, 2020-June 15, 2021
    This dataset updates: Every week
    Subnational data about Covid19 in Mauritania - Infections (new cases, gender), Deaths, Recoveries. Please note that the gender data is not available yet for every day, our teams are working on it. Thank you for your understanding.
  • 1200+ Downloads
    Updated 17 June 2021 | Dataset date: March 25, 2020-June 15, 2021
    This dataset updates: Every week
    Subnational data about Covid19 in Mali - Infections (new cases, gender), Deaths, Recoveries.
  • 1200+ Downloads
    Updated 17 June 2021 | Dataset date: March 19, 2020-June 16, 2021
    This dataset updates: Every week
    Subnational data about Covid19 in Niger - Infected (new cases, gender), Deceased, Recovered.
  • 40+ Downloads
    Updated 17 June 2021 | Dataset date: June 02, 2021-June 16, 2021
    This dataset updates: Every week
    Subnational Covid19 vaccination data in Burkina Faso per region from the beginning of the vaccination campaign. Vaccine type : AstraZeneca Details : first dose Covax initiative Please note that the architecture might change in the near future to add 2nd doses information
  • 1100+ Downloads
    Updated 17 June 2021 | Dataset date: March 09, 2020-June 16, 2021
    This dataset updates: Every week
    Subnational data about Covid19 in Burkina Faso - Infected (new cases, gender), Deceased, Recovered. NEW (!) : VACCINATION DATA PER REGION
  • Updated 16 June 2021 | Dataset date: June 01, 2019-August 31, 2019
    This data is by request only
    Primary data will be collected by means of a household-level survey designed with the participation of the humanitarian clusters in Somalia. Cluster leads are asked to outline information gaps and the type of data required to inform their strategic plans. Key indicators are developed by REACH with the substantive input of participating partners, and subsequently validated by the clusters. REACH will draft the household survey tool through an iterative consultation process with cluster partners and OCHA and is aligned, as much as possible, with the Joint Inter-Sectoral Analysis Framework (JIAF) which will serve as a common and structured method for assessing the severity of needs across different clusters. The assessment will use stratified cluster sampling at the district level using settlements as the clusters and households as the unit of measurement. For some districts, 2-stage stratified random sampling will be used instead of stratified cluster sampling for large urban centres, if it proves to be more efficient and logistically feasible for data collection. The sample will be stratified by population group, disaggregated by non-displaced communities, and IDP settlements; the sample will be further stratified by district to ensure coverage and comparison across the entire country (with the exception of inaccessible areas). In the case of cluster sampling, the minimum cluster size will be set to 6 households. The sample size will be adjusted for the design effect and will enable generalisation of the results to each of the two population strata in each district, with a 90% confidence level and a 10% margin of error.
  • 100+ Downloads
    Updated 16 June 2021 | Dataset date: February 15, 2021-June 19, 2021
    This dataset updates: Every two weeks
    Subnational dataset on the 2021 Ebola outbreak in Guinea including cases, deaths, hospitalisations, contact tracing, and vaccinations. The data is down to sub-prefecture level and updated approx 1-3 days since 15/02/21. The data has been extracted from the Government of Guinea situation reports, available from Relief Web. The dataset contains the following information: - Cas: Distribution of Ebola cases by health district, including suspected, confirmed, and probable cases. - Patients et des décès: Situation of patients and deaths in epidemiological treatment centres, including hospitalisations, discharges, and deaths. - Suivi des contacts: Status of contact tracing including number of contacts followed up, number not followed up, reasons for not following up, follow-up rate, contacts still remaining to follow up, and number of contacts who have become suspected cases. - Vaccinations: Vaccination results and targets in N’Zérékoré, including target contacts, daily vaccine contacts, number vaccinated (cumulative), and percent of target contacts who have been vaccinated. For more information, please see the README sheet of the dataset. For any questions, please contact ah@acaps.org. Read the ACAPS short note Ebola outbreak in Guinea: https://www.acaps.org/country/guinea/special-reports#container-1630
  • 20+ Downloads
    Updated 15 June 2021 | Dataset date: November 14, 2020-May 17, 2021
    This dataset updates: Every month
    Overview The dataset contains harmonized indicators created from high-frequency phone surveys collected by the World Bank and partners. The surveys capture the socioeconomic impacts of the COVID-19 pandemic on households and individuals from all developing regions. Data are available for over 90 indicators in 14 topic areas, including education, food security, income, safety nets, and others. For more information, please refer to our Technical Note and Data Dictionary. Unit of Measure Percentages. Aggregation Method: The data is aggregated by Urban/Rural/National and Industry Sector Disclaimer: This harmonized dataset is an ongoing collation and harmonization of COVID-19 high-frequency phone survey (HFPS) data. Harmonization involves redefining indicators and categories so that they are comparable across countries. As a result, even if the names and definitions of indicators appear similar, numbers in this global database might differ slightly from those of each country's publications or dashboard. If you see large discrepancies or other issues, please reach out. Version Notes: COVID-19 Harmonized Household Data Feb 18 • Temporarily suppressed select income, labor, and government assistance indicators collected after wave 2 surveys for harmonization review • Added need for, and access to medical care in multiple countries • Temporarily suppressed select income, labor and government assistance indicators collected after wave 2 surveys for harmonization review Funding Name, Abbreviation, Role: The project received support from the Trust Fund for Statistical Capacity Building III (TFSCB-III). TFSCB-III is funded by the United Kingdom’s Foreign, Commonwealth & Development Office, the Department of Foreign Affairs and Trade of Ireland, and the Governments of Canada and Korea. Other Acknowledgments: This dashboard was created by the Data for Goals (D4G) team and the Regional High-Frequency Phone Survey (HFPS) Focal Points in the EFI Poverty and Equity Global Practice (POV GP), under the guidance of POV GP management, using data collected under the World Bank-wide COVID-19 HFPS initiative. Time Periods: March, 2021
  • 1900+ Downloads
    Updated 15 June 2021 | Dataset date: January 01, 1975-December 31, 2019
    This dataset updates: Every month
    Contains data from World Health Organization's data portal covering the following categories: Mortality and global health estimates, Sustainable development goals, Millennium Development Goals (MDGs), Health systems, Malaria, Tuberculosis, Child health, Infectious diseases, World Health Statistics, Health financing, Public health and environment, Substance use and mental health, Tobacco, Injuries and violence, HIV/AIDS and other STIs, Nutrition, Urban health, Noncommunicable diseases, Noncommunicable diseases CCS, Negelected tropical diseases, Health Equity Monitor, Infrastructure, Essential health technologies, Medical equipment, Demographic and socioeconomic statistics, TOBACCO, Neglected tropical diseases, International Health Regulations (2005) monitoring framework, Insecticide resistance, Oral health, Universal Health Coverage, UHC, Global Observatory for eHealth (GOe), RSUD: GOVERNANCE, POLICY AND FINANCING : PREVENTION, RSUD: GOVERNANCE, POLICY AND FINANCING: TREATMENT, RSUD: GOVERNANCE, POLICY AND FINANCING: FINANCING, RSUD: SERVICE ORGANIZATION AND DELIVERY: TREATMENT SECTORS AND PROVIDERS, RSUD: SERVICE ORGANIZATION AND DELIVERY: TREATMENT CAPACITY AND TREATMENT COVERAGE, RSUD: SERVICE ORGANIZATION AND DELIVERY: PHARMACOLOGICAL TREATMENT, RSUD: SERVICE ORGANIZATION AND DELIVERY: SCREENING AND BRIEF INTERVENTIONS, RSUD: SERVICE ORGANIZATION AND DELIVERY: PREVENTION PROGRAMS AND PROVIDERS, RSUD: SERVICE ORGANIZATION AND DELIVERY: SPECIAL PROGRAMMES AND SERVICES, RSUD: HUMAN RESOURCES, RSUD: INFORMATION SYSTEMS, RSUD: YOUTH, FINANCIAL PROTECTION, AMR GLASS, Noncommunicable diseases and mental health, Health workforce, Neglected Tropical Diseases, AMR GASP, ICD, SEXUAL AND REPRODUCTIVE HEALTH, Immunization, NLIS For links to individual indicator metadata, see resource descriptions.
  • 4100+ Downloads
    Updated 15 June 2021 | Dataset date: January 01, 1961-December 31, 2019
    This dataset updates: Every month
    Contains data from World Health Organization's data portal covering the following categories: Mortality and global health estimates, Sustainable development goals, Millennium Development Goals (MDGs), Health systems, Malaria, Tuberculosis, Child health, Infectious diseases, World Health Statistics, Health financing, Public health and environment, Substance use and mental health, Tobacco, Injuries and violence, HIV/AIDS and other STIs, Nutrition, Urban health, Noncommunicable diseases, Noncommunicable diseases CCS, Negelected tropical diseases, Health Equity Monitor, Infrastructure, Essential health technologies, Medical equipment, Demographic and socioeconomic statistics, TOBACCO, Neglected tropical diseases, International Health Regulations (2005) monitoring framework, Insecticide resistance, Oral health, Universal Health Coverage, UHC, Global Observatory for eHealth (GOe), RSUD: GOVERNANCE, POLICY AND FINANCING : PREVENTION, RSUD: GOVERNANCE, POLICY AND FINANCING: TREATMENT, RSUD: GOVERNANCE, POLICY AND FINANCING: FINANCING, RSUD: SERVICE ORGANIZATION AND DELIVERY: TREATMENT SECTORS AND PROVIDERS, RSUD: SERVICE ORGANIZATION AND DELIVERY: TREATMENT CAPACITY AND TREATMENT COVERAGE, RSUD: SERVICE ORGANIZATION AND DELIVERY: PHARMACOLOGICAL TREATMENT, RSUD: SERVICE ORGANIZATION AND DELIVERY: SCREENING AND BRIEF INTERVENTIONS, RSUD: SERVICE ORGANIZATION AND DELIVERY: PREVENTION PROGRAMS AND PROVIDERS, RSUD: SERVICE ORGANIZATION AND DELIVERY: SPECIAL PROGRAMMES AND SERVICES, RSUD: HUMAN RESOURCES, RSUD: INFORMATION SYSTEMS, RSUD: YOUTH, FINANCIAL PROTECTION, AMR GLASS, Noncommunicable diseases and mental health, Health workforce, Neglected Tropical Diseases, AMR GASP, ICD, SEXUAL AND REPRODUCTIVE HEALTH, Immunization, NLIS For links to individual indicator metadata, see resource descriptions.
  • 1900+ Downloads
    Updated 15 June 2021 | Dataset date: January 01, 1981-December 31, 2019
    This dataset updates: Every month
    Contains data from World Health Organization's data portal covering the following categories: Mortality and global health estimates, Sustainable development goals, Millennium Development Goals (MDGs), Health systems, Malaria, Tuberculosis, Child health, Infectious diseases, World Health Statistics, Health financing, Public health and environment, Substance use and mental health, Tobacco, Injuries and violence, HIV/AIDS and other STIs, Nutrition, Urban health, Noncommunicable diseases, Noncommunicable diseases CCS, Negelected tropical diseases, Health Equity Monitor, Infrastructure, Essential health technologies, Medical equipment, Demographic and socioeconomic statistics, TOBACCO, Neglected tropical diseases, International Health Regulations (2005) monitoring framework, Insecticide resistance, Oral health, Universal Health Coverage, UHC, Global Observatory for eHealth (GOe), RSUD: GOVERNANCE, POLICY AND FINANCING : PREVENTION, RSUD: GOVERNANCE, POLICY AND FINANCING: TREATMENT, RSUD: GOVERNANCE, POLICY AND FINANCING: FINANCING, RSUD: SERVICE ORGANIZATION AND DELIVERY: TREATMENT SECTORS AND PROVIDERS, RSUD: SERVICE ORGANIZATION AND DELIVERY: TREATMENT CAPACITY AND TREATMENT COVERAGE, RSUD: SERVICE ORGANIZATION AND DELIVERY: PHARMACOLOGICAL TREATMENT, RSUD: SERVICE ORGANIZATION AND DELIVERY: SCREENING AND BRIEF INTERVENTIONS, RSUD: SERVICE ORGANIZATION AND DELIVERY: PREVENTION PROGRAMS AND PROVIDERS, RSUD: SERVICE ORGANIZATION AND DELIVERY: SPECIAL PROGRAMMES AND SERVICES, RSUD: HUMAN RESOURCES, RSUD: INFORMATION SYSTEMS, RSUD: YOUTH, FINANCIAL PROTECTION, AMR GLASS, Noncommunicable diseases and mental health, Health workforce, Neglected Tropical Diseases, AMR GASP, ICD, SEXUAL AND REPRODUCTIVE HEALTH, Immunization, NLIS For links to individual indicator metadata, see resource descriptions.