This dataset contains the number of suspected cases, confirmed cases, and deaths by Département due to the Coronavirus pandemic in Haiti. Released by the Ministry of Public Health and Population of Haiti.
July 1, 2020
| Dataset date: Jan 24, 2020-Jul 1, 2020
This dataset contains the number of confirmed cases, recoveries and deaths by country and subnational region due to the Coronavirus pandemic in Europe.
Since the outbreak of the COVID-19 crisis, the Joint Research Centre (JRC) has been supporting the European Commission in multidisciplinary areas to understand the COVID-19 emergency, anticipate its impacts, and support contingency planning.
This data provides an overview of the monitoring in the area of the 34 UCPM Participating States plus Switzerland related to sub-national data (admin level 1) on numbers of contagious and fatalities by COVID-19, collected directly from the National Authoritative sources (National monitoring websites, when available).
The sub-national granularity of the data allows to have a fit-for-purpose model to early capture the local spread and response to the COVID-19 outbreak.
The data is maintained on the JRC COVID-19 Github Repository
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 can be found in the us-states.csv file.
County-level data can be found in the us-counties.csv file.
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.
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
June 26, 2020
| Dataset date: Jun 24, 2020
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
June 17, 2020
| Dataset date: May 31, 2020
Aggregated figures for Natural Disasters in EM-DAT
More on the EM-DAT database : ( website / data portal ).
Each line corresponds to a given combination of year, country, disaster subtype and reports figures for :
number of disasters
total number of people affected
total number of deaths
economic losses (original value and adjusted)
June 12, 2020
| Dataset date: Mar 1, 2020-Apr 30, 2020
This datasets has IDPs, Household & Returnees data at Admin3 level gathered through DTM Mobility Tracking Assessment.
In the context of the political instability that has prevailed since the uprising in Libya (October 2011) and culminated in the collapse of a fragile central authority accompanied by fragmentation and infighting among myriads of militias, with continued fighting since the mid-2014 escalations, estimates indicate that the number of Internally Displaced Per-sons (IDPs) in Libya has exceeded 400,000 individuals, some eight percent of the total population (HNO, September 2015). While the country struggles to achieve and maintain stability, thousands of migrants are also taking journeys to and through Libya in a desperate bid to seek a better life in Europe. These migrants are exposed to risks of being trafficked and exploited while traveling through dangerous routes in deserts and territories controlled by different armed groups, as well as dying during attempts to cross the Mediterranean Sea.
However, there has been no standardized mechanism in place to verify and regularly update IDP and migrant numbers. Given that most humanitarian and international organizations operate remotely from Tunis since mid-July 2014 due to the deteriorating security situation, maintaining access to reliable and updated data on the humanitarian situation in Libya has been challenging.
June 11, 2020
| Dataset date: Apr 30, 2020
The dataset contains IDPs individual and households at admin2 level. IOM has been developing a Displacement Tracking Matrix (DTM) since May 2015 aimed at effectively monitoring and evaluating the flows of Burundian IDPs and providing accurate information on the current IDP situation.
The DTM in Burundi has been successfully used in 2014, upon the request of the humanitarian community and the GoB when some areas of Bujumbura were flooded, which caused displacement. This tool allowed registering IDPs in four IDP sites and in host families in four locations and identifying their humanitarian needs.
The dataset contains IDPs, returnees at sub national level. The dataset also has reason of displacement, origin and dates of multiple displacements.
The context of displacement in Mali remains complex and fluid. Movements of IDPs currently residing in the southern regions to the northern regions continue to be reported. While some have indicated that they have returned definitively, other IDPs say they travel back and forth between the place of travel and the place of origin.