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  • 900+ Downloads
    Updated Live | Dataset date: Dec 1, 2019-Apr 10, 2020
    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.
  • 40+ Downloads
    Updated April 10, 2020 | Dataset date: Apr 10, 2020
    This dataset updates: Every week
    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 April 10, 2020 | Dataset date: Jan 19, 2015
    This dataset updates: Never
    (DEPRECATED: please use the data at https://data.humdata.org/dataset/global-coordination-groups-beta instead — also available at http://vocabulary.unocha.org). Based on feedback from Humanitarian Information Management community, the Humanitarianresponse.info team in OCHA has released three letters standard clusters codes via APIs to help facilitate interoperability between websites and data from several humanitarian platforms including HumanitarianRespons.info, ReliefWeb, HDX, Online Reporting System, Online Project System, Financial Tracking Service and the developer communities.
  • 2900+ Downloads
    Updated April 9, 2020 | Dataset date: Apr 9, 2020
    This dataset updates: Every day
    This dataset contains key figures (topline numbers) on the world's most pressing humanitarian crises. The data, curated by ReliefWeb's editorial team based on its relevance to the humanitarian community, is updated regularly. The description of the files and columns can be found in the additional metadata spreadsheet file.
  • 1000+ Downloads
    Updated April 9, 2020 | Dataset date: Mar 10, 2020-Apr 30, 2020
    This dataset updates: Every day
    This data has been collected from various sources and is displayed in this online dashboard: http://arcg.is/uHyuO Mobile version: http://arcg.is/0q8Xfj 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
  • 3900+ Downloads
    Updated April 9, 2020 | Dataset date: Mar 17, 2020
    This dataset updates: As needed
    The #COVID19 Government Measures Dataset puts together all the measures implemented by governments worldwide in response to the Coronavirus pandemic. Data collection includes secondary data review. The researched information available falls into five categories: Social distancing Movement restrictions Public health measures Social and economic measures Lockdowns Each category is broken down into several types of measures. ACAPS consulted government, media, United Nations, and other organisations sources. For any comments, please contact us at info@acaps.org Please note note that some measures together with non-compliance policies may not be recorded and the exact date of implementation may not be accurate in some cases, due to the different way of reporting of the primary data sources we used.
  • 200+ Downloads
    Updated Live | Dataset date: Jan 1, 2020-Apr 10, 2020
    This dataset updates: Live
    Governments are taking a wide range of measures in response to the COVID-19 outbreak. The Oxford COVID-19 Government Response Tracker (OxCGRT) aims track and compare government responses to the coronavirus outbreak worldwide rigorously and consistently. The OxCGRT systematically collects information on several different common policy responses governments have taken, scores the stringency of such measures, and aggregates these scores into a common Stringency Index. For more, please visit > https://www.bsg.ox.ac.uk/research/research-projects/oxford-covid-19-government-response-tracker
  • 100+ Downloads
    Updated April 8, 2020 | Dataset date: Apr 2, 2020
    This dataset updates: Every week
    The current outbreak of COVID-19 has affected global mobility in the form of various travel disruptions, restrictions and blockages. To better understand how COVID-19 affects global mobility, the International Organization for Migration (IOM) has been working to map the impacts on human mobility, at Global, Regional and Country level. Using direct input from IOM missions, this dashboard displays updated mobility restrictions at location level (airport, land border points, sea border points, internal transit points). For each point of entry, data is collected on: type of restriction, measured applied & timeframe, population category that might be affected from the measures.
  • 20+ Downloads
    Updated Live | Dataset date: Apr 6, 2020
    This dataset updates: Live
    List of realtime notices for international airports and airspace referring to COVID-19 restrictions
  • 1200+ Downloads
    Updated April 7, 2020 | Dataset date: Dec 10, 2019
    This dataset updates: Every month
    The INFORM Severity Index is a regularly updated, and easily interpreted model for measuring the severity of humanitarian crisis globally. It is a composite index, which brings together 31 core indicators, organised in three dimensions: impact, conditions of affected people, and complexity. All the indicators are scored on a scale of 1 to 5. These scores are then aggregated into components, the three dimensions (Impact, Conditions, Complexity), and the overall severity category based on the analytical framework. The three dimensions have been weighted according to their contribution to severity: impact of the crisis (20%); conditions of affected people (50%); complexity (30%). The weightings are currently a best estimate and will be refined using expert analysis and statistical methods. Each crisis will fall into 1 of 5 categories based on their score ranging from very low to high. ACAPS – an INFORM technical partner – is responsible for collection, cleaning, analysis and input of data into the model and the production of the final results. Read more on the GCSI methodology here: https://www.acaps.org/methodology/severity
  • 100+ Downloads
    Updated April 6, 2020 | Dataset date: Feb 1, 2020-Mar 24, 2020
    This dataset updates: Every month
    This dataset represents the geographical distribution of Twitter users and tweets related to Coronavirus (COVID-19) pandemic at three levels. The data was collected and processed by the AIDR system (http://aidr.qcri.org). See the individual resources/files for more details about the datasets.
  • 90+ Downloads
    Updated April 6, 2020 | Dataset date: Jan 1, 2008-Dec 31, 2018
    This dataset updates: As needed
    Internally displaced persons are defined according to the 1998 Guiding Principles (http://www.internal-displacement.org/publications/1998/ocha-guiding-principles-on-internal-displacement) as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border. "New Displacement" refers to the number of new cases or incidents of displacement recorded, rather than the number of people displaced. This is done because people may have been displaced more than once. Contains data from IDMC's Global Internal Displacement Database.
  • 100+ Downloads
    Updated April 6, 2020 | Dataset date: Jan 1, 2008-Dec 31, 2018
    This dataset updates: As needed
    Internally displaced persons are defined according to the 1998 Guiding Principles (http://www.internal-displacement.org/publications/1998/ocha-guiding-principles-on-internal-displacement) as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border. "People Displaced" refers to the number of people living in displacement as of the end of each year. Contains data from IDMC's Global Internal Displacement Database.
  • 100+ Downloads
    Updated April 3, 2020 | Dataset date: Apr 10, 2020
    This dataset updates: Every week
    This dataset lists all contributions made by donors to the Central Emergency Response Fund (CERF). CERF receives broad support from United Nations Member States, observers, regional governments and international organizations, and the private sector, including corporations, non-governmental organizations and individuals.
  • 128000+ Downloads
    Updated Live | Dataset date: Jan 22, 2020-Apr 3, 2020
    This dataset updates: Live
    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. Pneumonia. 2020, 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). JSU 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
  • 1800+ Downloads
    Updated April 1, 2020 | Dataset date: Mar 31, 2020
    This dataset updates: Every year
    Child malnutrition joint country dataset (UNICEF, WHO, World Bank Group) Definitions: Severe Wasting: Percentage of children aged 0–59 months who are below minus three standard deviations from median weight-for-height of the WHO Child Growth Standards. Wasting – Moderate and severe: Percentage of children aged 0–59 months who are below minus two standard deviations from median weight-for-height of the WHO Child Growth Standards. Overweight – Moderate and severe: Percentage of children aged 0-59 months who are above two standard deviations from median weight-for-height of the WHO Child Growth Standards. Stunting – Moderate and severe: Percentage of children aged 0–59 months who are below minus two standard deviations from median height-for-age of the WHO Child Growth Standards. Underweight – Moderate and severe: Percentage of children aged 0–59 months who are below minus two standard deviations from median weight-for-age of the World Health Organization (WHO) Child Growth Standards.
  • 100+ Downloads
    Updated April 1, 2020 | Dataset date: Mar 31, 2020
    This dataset updates: Every year
    Child malnutrition joint global and regional dataset (UNICEF, WHO, World Bank Group) Definitions: Severe Wasting: Percentage of children aged 0–59 months who are below minus three standard deviations from median weight-for-height of the WHO Child Growth Standards. Wasting – Moderate and severe: Percentage of children aged 0–59 months who are below minus two standard deviations from median weight-for-height of the WHO Child Growth Standards. Overweight – Moderate and severe: Percentage of children aged 0-59 months who are above two standard deviations from median weight-for-height of the WHO Child Growth Standards. Stunting – Moderate and severe: Percentage of children aged 0–59 months who are below minus two standard deviations from median height-for-age of the WHO Child Growth Standards. Underweight – Moderate and severe: Percentage of children aged 0–59 months who are below minus two standard deviations from median weight-for-age of the World Health Organization (WHO) Child Growth Standards.
  • 600+ Downloads
    Updated Live | Dataset date: Dec 31, 2019-Apr 1, 2020
    This dataset updates: Live
    Data collected by the European Centre for Disease Prevention and Control. The downloadable data file is updated daily and contains the latest available public data on COVID-19. Public-use data files allows users to manipulate the data in a format appropriate for their analyses. Users of ECDC public-use data files must comply with data use restrictions to ensure that the information will be used solely for statistical analysis or reporting purposes. For further information, visit https://www.ecdc.europa.eu/en/novel-coronavirus-china.
  • 200+ Downloads
    Updated March 31, 2020 | Dataset date: Mar 31, 2020
    This dataset updates: As needed
    The INFORM EPIDEMIC RISK INDEX assesses the risk of countries to epidemic outbreak, which would exceed the national capacity to respond to the crisis.
  • 100+ Downloads
    Updated March 31, 2020 | Dataset date: Mar 26, 2020
    This dataset updates: As needed
    COVID-19 Travel Restriction Monitoring - Using secondary data sources, such as the International Air Transport Association (IATA), media reports and information direct from IOM missions, this platform maps and analyzes the various country, territories and areas imposing restrictions, and those with restrictions being imposed upon them, all categorized by restriction type. All analyses is presented at country level.
  • 3600+ Downloads
    Updated March 25, 2020 | Dataset date: Mar 24, 2020
    This dataset updates: Every month
    Peacekeeping Uniformed Contributions by Rank of Troop- or Police-Contributing Country, as of end of last calendar month, associated with unique ID, Country ISO Code, M49 DESA code, Country Name of Troop or Police Contributing country, Rank for the Month, Number of male uniformed personnel, Number of female uniformed personnel, and Monthly report Date. This data set will be updated monthly.
  • 1700+ Downloads
    Updated March 25, 2020 | Dataset date: Mar 24, 2020
    This dataset updates: Every month
    Peacekeeping Uniformed Contributions by Gender, as of end of last calendar month, associated with unique OD, Country ISO Code, M49 DESA code, Country Name of Troop or Police Contributing country, Mission, Description of uniformed category, Gender, and Monthly report Date. This data set will be update monthly.
  • 1600+ Downloads
    Updated March 25, 2020 | Dataset date: Mar 24, 2020
    This dataset updates: Every month
    This set includes data on fatalities in UN peacekeeping operations. It includes a unique casualty identifier, the incident date, the mission acronym, the type of casualty, the ISO code associated with the country of origin of the personnel, the relevant M49 DESA code, the type pf personnel involved, and the type of incident.
  • 500+ Downloads
    Updated Live | Dataset date: Feb 16, 2020-Apr 10, 2020
    This dataset updates: Live
    The number of children, youth and adults not attending schools or universities because of COVID-19 is soaring. Governments all around the world have closed educational institutions in an attempt to contain the global pandemic. According to UNESCO monitoring, over 100 countries have implemented nationwide closures, impacting over half of world’s student population. Several other countries have implemented localized school closures and, should these closures become nationwide, millions of additional learners will experience education disruption.
  • 2500+ Downloads
    Updated March 24, 2020 | Dataset date: Jan 1, 1960-Dec 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.