Updated
March 28, 2020
| Dataset date: Dec 1, 2019-Mar 28, 2020
This dataset updates: Every day
Data Overview
This repository contains spatiotemporal data from many official sources for 2019-Novel Coronavirus beginning 2019 in Hubei, China ("nCoV_2019")
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.
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.
Updated
March 23, 2020
| Dataset date: Mar 28, 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.
Updated
March 28, 2020
| Dataset date: Jan 1, 1992-Dec 31, 1995
This dataset updates: As needed
This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University
Updated
March 28, 2020
| Dataset date: Jan 1, 2008-Dec 31, 2017
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.
"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.
Updated
March 27, 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
Updated
March 28, 2020
| Dataset date: 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 South Africa: (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
March 28, 2020
| Dataset date: 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 Togo: (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
March 28, 2020
| Dataset date: Oct 1, 2018
This dataset updates: As needed
The population of the world, allocated to 1 arcsecond blocks. This refines CIESIN’s Gridded Population of the World project, using machine learning models on high-resolution worldwide Digital Globe satellite imagery. More information.
There is also a tiled version of this dataset that may be easier to use if you are interested in many countries.
Updated
March 28, 2020
| Dataset date: 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 Sao Tome and Principe: (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
March 28, 2020
| Dataset date: 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 Sierra Leone: (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.
Updated
March 28, 2020
| Dataset date: Mar 28, 2020
This dataset updates: Every day
FTS publishes data on humanitarian funding flows as reported by donors and recipient organizations. It presents all humanitarian funding to a country and funding that is specifically reported or that can be specifically mapped against funding requirements stated in humanitarian response plans. The data comes from OCHA's Financial Tracking Service, is encoded as utf-8 and the second row of the CSV contains HXL tags.
Updated
March 28, 2020
| Dataset date: 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 Mayotte: (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
January 27, 2020
| Dataset date: Jan 1, 2001-Dec 31, 2016
This dataset updates: As needed
This layer contains information about the land degradation phenomenon - by second-level administrative area - observed for the purposes of the Integrated Context Analysis (ICA) run in Afghanistan in 2019.
Data sources: HQ OSE GIS Analysis of NASA MODIS 2001-2016, WorldClim 1970-2000, FAO and NASA SRTM Digital Elevation Model data.
The main indicators used for the analysis were the average ecological changes observed between 2001-2006 and 2011-2016 and the percentage of erosion-prone surface.
Original dataset title: ICA Afghanistan, 2019 - Land Degradation, 2001-2016
Updated
March 28, 2020
| Dataset date: 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 Namibia: (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
March 28, 2020
| Dataset date: 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 Mozambique: (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
March 28, 2020
| Dataset date: 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 Lesotho: (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
March 4, 2020
| Dataset date: 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 Liberia: (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.
Updated
November 6, 2019
| Dataset date: Mar 27, 2020
This dataset updates: Every week
This dataset lists project funding allocations from OCHA's Central Emergency Response Fund (CERF). CERF allocations are made to ensure a rapid response to sudden-onset emergencies or to rapidly deteriorating conditions in an existing emergency and to support humanitarian response activities within an underfunded emergency.
Updated
March 27, 2020
| Dataset date: Jun 1, 2017-Feb 1, 2019
This dataset updates: Every month
This dashboard highlights the living situation in Syria by showing the prices of basic market items.
How to use this product:
The first three pages track price change chronologically on governorate level, with ability to compare between them by choosing one or more.
The subsequent pages show the prices of market items on the governorate and sub-district level with an item availability heat map of any selected item on any selected level and period. You can select one of the listed items in one sub-district or more. When you choose a governorate its subdistrict(s) will be highlighted according to the availability of the selected item in the selected governorate(s).
Updated
March 25, 2020
| Dataset date: Mar 25, 2020
This dataset updates: As needed
This data is about the humanitarian activities by civil society organizations, clusters/sectors, government, private organizations, UN agencies and Red Cross related to COVID-19.
Updated
March 27, 2020
| Dataset date: Feb 23, 2020-Mar 13, 2020
This dataset updates: Every three months
The dataset contains estimates of changes in human mobility during the COVID-19 outbreak. Estimates are reported for three weeks since the start of the outbreak: February 22-28, February 29 - March 6, March 7-13.
These data underly the reports published at https://covid19mm.github.io/
If you use these data for your research please cite the following work:
COVID-19 outbreak response: a first assessment of mobility changes in Italy following national lockdown
Emanuele Pepe, Paolo Bajardi, Laetitia Gauvin, Filippo Privitera, Brennan Lake, Ciro Cattuto, Michele Tizzoni
medRxiv 2020.03.22.20039933; doi: https://doi.org/10.1101/2020.03.22.20039933
Updated
March 27, 2020
| Dataset date: Dec 31, 2019-Mar 23, 2020
This dataset updates: Every day
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.
Updated
February 28, 2017
| Dataset date: Nov 30, 2016
This dataset updates: Never
Kenya’s commitment to close down Dadaab by mid-2017, the world’s largest refugee camp hosting over 300,000 Somalis, is putting increasing pressure on service delivery and infrastructure in Somalia. By October 2016, an estimate of 31,226 Somali people have returned from Kenya, the majority of which are heading to Kismayo, Baidoa, Luuq and Mogadishu. While the caseload of returnees is expected to continue growing over the coming months, there is no clear understanding of movement patterns of returnees or internally displaced persons which further complicates the attempts by humanitarian organisations to provide for required services.
The absence of a CCCM cluster in Somalia means that information on humanitarian needs and gaps has been limited to ad-hoc and un-coordinated assessments. The Kismayo IDP settlement assessment was triggered as a result of the need for a multi-cluster, area-based and coordinated information approach for humanitarian planning and service delivery in informal IDP settlements in Somalia.
The Kismayo IDP Settlement Assessment was carried out with financial support from USAID-OFDA and ECHO and in close collaboration with clusters (WASH, Shelter & NFIs, Education, Food Security, Health, Nutrition and Protection).
