| Dataset date: Jan 22, 2020-May 28, 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, 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), and others. JHU 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:
The resources below ceased being updated on 22/03/2020 and were removed on 26/03/2020:
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:
Public health measures
Social and economic measures
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 firstname.lastname@example.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.
| Dataset date: Jan 18, 2020-May 29, 2020
This dataset updates: Live
'Our World in Data' is compiling COVID-19 testing data over time for many countries around the world. They are adding further data in the coming days as more details become available for other countries. In some cases figures refer to the number of tests, in other cases to the number of individuals who have been tested. Refer to documentation provided here.
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
| Dataset date: Jan 1, 2020-May 29, 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
The INFORM COVID-19 Risk Index is a composite index that identifies: “countries at risk from health and humanitarian impacts of COVID-19 that could overwhelm current national response capacity, and therefore lead to a need for additional international assistance”.
The INFORM COVID-19 Risk Index is primarily concerned with structural risk factors, i.e. those that existed before the outbreak. It can be used to support prioritization of preparedness and early response actions for the primary impacts of the pandemic, and identify countries where secondary impacts are likely to have the most critical humanitarian consequences.
The main scope of the INFORM COVID-19 Risk Index is global and regional risk-informed resource allocation, i.e. where comparable understanding of countries’ risk is important. It cannot predict the impacts of the pandemic in individual countries. It does not consider the mechanisms behind secondary impacts - for example how a COVID-19 outbreak could increase conflict risk.
May 26, 2020
| Dataset date: Dec 31, 2019-May 26, 2020
This dataset updates: Every week
The Database of Government Actions on COVID-19 in Developing Countries collates and tracks national policies and actions in response to the pandemic, with a focus on developing countries.
The database provides information for 20 Global South countries – plus 6 Global North countries for reference – that Dalberg staff are either based in or know well. The database content is drawn from publicly available information combined, crucially, with on-the-ground knowledge of Dalberg staff.
The database contains a comprehensive set of 100 non-pharmaceutical interventions – organized in a framework intended to make it easy to observe common variations between countries in the scope and extent of major interventions. Interventions we are tracking include:
• Health-related: strengthening of healthcare systems, detection and isolation of actual / possible cases, quarantines
• Policy-related: government coordination and legal authorization, public communications and education, movement restrictions
• Distancing and hygiene: social distancing measures, movement restrictions, decontamination of physical spaces
• Economic measures: economic and social measures, logistics / supply chains and security.
We hope the database will be a useful resource for several groups of users: (i) governments and policymakers looking for a quick guide to actions taken by different countries—including a range of low- and middle-income countries, (ii) policy analysts and researchers studying the data to identify patterns of actions taken and compare the effectiveness of different interventions in curbing the pandemic, and (iii) media and others seeking to quickly access facts about the actions taken by governments in the countries covered in the database.
Comments on the data can be submitted to email@example.com
Questions can be submitted to firstname.lastname@example.org
May 29, 2020
| Dataset date: Mar 10, 2020-May 28, 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: email@example.com
| Dataset date: Dec 31, 2019-May 29, 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.
May 29, 2020
| Dataset date: Jan 24, 2020-May 29, 2020
This dataset updates: Every day
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
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
May 15, 2020
| Dataset date: Feb 1, 2020-May 1, 2020
This dataset updates: Every three months
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.
| Dataset date: Feb 16, 2020-May 29, 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.
March 31, 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
INFORM is a multi-stakeholder forum for developing shared, quantitative analysis relevant to humanitarian crises and disasters. INFORM includes organisations from across the multilateral system, including the humanitarian and development sector, donors, and technical partners. The Joint Research Center of European Commission is the scientific and technical lead for INFORM. In response to the COVID-19 pandemic, INFORM has released a COVID Risk Index to support the specific decision-making needs of humanitarian and other organisations.
The following is an analysis that addresses key questions for humanitarian organisations by combining information from 3 INFORM products:
• The new INFORM COVID Risk Index, which identifies “countries at risk from health and humanitarian impacts of COVID-19 that could overwhelm current national response capacity, and therefore lead to a need for additional international assistance”. The INFORM COVID Risk Index is
• The INFORM Risk Index (Mid-2020 version), which identifies “countries at risk from humanitarian emergencies that could overwhelm current national response capacity, and therefore lead to a need for international assistance”. The INFORM Risk Index takes into account natural and human hazards, as well as vulnerability and lack of coping capacity.
• The INFORM Severity Index (March 2020 version) is a regularly updated model for measuring the severity of humanitarian crises globally, which brings together indicators of impact, conditions of affected people, and complexity.
Global Humanitarian Response Plan COVID-19 administrative level 1 boundaries, gazetteer and population tables for countries covered by the May update of the Global Humanitarian Response Plan COVID-19.
Population statistics are available for 37 of the 63 countries or territories.
ADM1_PCODE: Administrative level 1 (various types) P-code
ADM0_PCODE: Administrative level 0 (country or territory) P-code
alpha_3: ISO 3166-1 Alpha 3 country or territory identifier
ADM0_REF: Administrative level 0 (country or territory) reference name (Latin script without special characters)
ADM1_REF: Administrative level 1 (various types) reference name (Latin script without special characters)
Population: Most recent available total population.
PLEASE SEE CAVEATS
| Dataset date: Dec 1, 2019-May 28, 2020
This dataset updates: Live
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 firstname.lastname@example.org 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 (email@example.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.
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.
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.
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.
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With the understanding that:
Waiver — Any of the above conditions can be waived if you get permission from the copyright holder.
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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/
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.
May 26, 2020
| Dataset date: Mar 1, 2020-Apr 30, 2020
This dataset updates: As needed
This dataset includes the latest available information on COVID-19 developments impacting the security of aid work and operations to help aid agencies meet duty of care obligations to staff and reach people in need.