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  • 5100+ Downloads
    Updated Live | Dataset date: December 01, 2019-May 08, 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.
  • 7100+ Downloads
    Updated 8 May 2021 | Dataset date: March 10, 2020-May 08, 2021
    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
  • 500+ Downloads
    Updated 8 May 2021 | Dataset date: January 01, 2013-December 31, 2013
    This dataset updates: Never
    1) Natural disaster events include avalanches, extreme winter conditions, flooding, heavy rainfall, landslides & mudflows, and extreme weather (sandstorms, hail, wind, etc) as recorded by OCHA field offices and IOM Afghanistan Humanitarian Assistance Database (HADB). 2) A natural disaster incident is defined as an event that has affected (i.e. impacted) Afghans, who may or may not require humanitarian assistance. 3) HADB information is used as a main reference and supplemented by OCHA Field Office reports for those incidents where information is not available from the HADB. OCHA information includes assessment figures from OCHA, ANDMA, Red Crescent Societies, national NGOs, international NGOs, and ERM.
  • 1600+ Downloads
    Updated 8 May 2021 | Dataset date: January 01, 2018-January 01, 2019
    This dataset updates: Never
    This dataset contains shapefiles for Guinea, Liberia, and Sierra Leone from the OpenStreetMap (OSM) project. Each country has its individual file. The dataset counts with contributions of hundreds of users. This dataset is updated daily. The original dataset can be downloaded from the OSM West Africa Ebola response wiki.
  • 1500+ Downloads
    Updated 7 May 2021 | Dataset date: October 15, 2020-October 15, 2020
    This dataset updates: Every week
    This bucket contains FAIR COVID-19 US county level forecast data
  • 600+ Downloads
    Updated 6 May 2021 | Dataset date: September 19, 2019-September 19, 2019
    This dataset updates: As needed
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Switzerland: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).
  • 900+ Downloads
    Updated 6 May 2021 | Dataset date: May 20, 2019-May 20, 2019
    This dataset updates: As needed
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Djibouti: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).
  • 600+ Downloads
    Updated 6 May 2021 | Dataset date: September 19, 2019-September 19, 2019
    This dataset updates: As needed
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Czechia: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).
  • 1000+ Downloads
    Updated 6 May 2021 | Dataset date: May 20, 2019-May 20, 2019
    This dataset updates: As needed
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Côte d'Ivoire: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).
  • 900+ Downloads
    Updated 6 May 2021 | Dataset date: June 10, 2019-June 10, 2019
    This dataset updates: As needed
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Costa Rica: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).
  • 900+ Downloads
    Updated 6 May 2021 | Dataset date: June 10, 2019-June 10, 2019
    This dataset updates: As needed
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Chile: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).
  • 1000+ Downloads
    Updated 5 May 2021 | Dataset date: May 20, 2019-May 20, 2019
    This dataset updates: As needed
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in the Central African Republic: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49). There is also a tiled version of this dataset that may be easier to use if you are interested in many countries.
  • 1100+ Downloads
    Updated 5 May 2021 | Dataset date: May 20, 2019-May 20, 2019
    This dataset updates: As needed
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Cameroon: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).
  • 600+ Downloads
    Updated 5 May 2021 | Dataset date: September 19, 2019-September 19, 2019
    This dataset updates: As needed
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Bulgaria: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).
  • 800+ Downloads
    Updated 5 May 2021 | Dataset date: May 20, 2019-May 20, 2019
    This dataset updates: As needed
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Cabo Verde: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).
  • 900+ Downloads
    Updated 5 May 2021 | Dataset date: May 20, 2019-May 20, 2019
    This dataset updates: As needed
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Botswana: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).
  • 1400+ Downloads
    Updated 5 May 2021 | Dataset date: June 10, 2019-June 10, 2019
    This dataset updates: As needed
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Bolivia: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).
  • 700+ Downloads
    Updated 5 May 2021 | Dataset date: June 19, 2019-June 19, 2019
    This dataset updates: As needed
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Bhutan: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).
  • 700+ Downloads
    Updated 5 May 2021 | Dataset date: June 10, 2019-June 10, 2019
    This dataset updates: As needed
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Belize: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).
  • 500+ Downloads
    Updated 5 May 2021 | Dataset date: September 19, 2019-September 19, 2019
    This dataset updates: As needed
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Belarus: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).
  • 800+ Downloads
    Updated 5 May 2021 | Dataset date: June 10, 2019-June 10, 2019
    This dataset updates: As needed
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in the Bahamas: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).
  • Updated 4 May 2021 | Dataset date: August 28, 2011-August 28, 2011
    This dataset updates: Never
    This is "Flood vectors - ASAR WSM (28 August 2011)" of the Flood analysis for Cambodia which began on 12 October 2011. It includes 787 satellite detected water bodies with a spatial extent of 35,971.59 square kilometers derived from the ASAR WSM image acq...
  • 10+ Downloads
    Updated 4 May 2021 | Dataset date: August 25, 2013-August 25, 2013
    This dataset updates: Never
    This is "Flood vectors - SPOT-5 (25 August 2013)" of the Flood analysis for Pakistan which began on 22 August 2013. It includes 924 satellite detected water bodies with a spatial extent of 581.47 square kilometers derived from the SPOT-5 image acquired on...
  • 10+ Downloads
    Updated 4 May 2021 | Dataset date: August 26, 2013-August 26, 2013
    This dataset updates: Never
    This is "Flood vectors - RISAT-1 (26 August 2013)" of the Flood analysis for Pakistan which began on 22 August 2013. It includes 1,076 satellite detected water bodies with a spatial extent of 3,229.89 square kilometers derived from the RISAT-1 image acqui...
  • 30+ Downloads
    Updated 4 May 2021 | Dataset date: September 21, 2011-September 21, 2011
    This dataset updates: Never
    This is "Flood vectors - TerraSAR-X (21 September 2011)" of the Flood analysis for Cambodia which began on 12 October 2011. It includes 12,718 satellite detected water bodies with a spatial extent of 1,930.64 square kilometers derived from the TerraSAR-X ...