COVID-19 Pandemic

While communities around the world face COVID-19, health authorities have revealed the same type of aggregated and anonymized information that they use in products like Google Maps could help them make fundamental decisions to combat COVID-19. The purpose of these Local Mobility Reports is to provide valuable information on the changes that have occurred in the mobility of people as a consequence of the policies that have been established to combat COVID - 19. These reports detail the movement trends as Over time arranged by geographical areas and classified into various categories of places, such as shops and leisure spaces, supermarkets and pharmacies, parks, public transport stations, workplaces and residential areas. More information in: https://www.google.com/covid19/mobility/

This dataset contains the number of confirmed cases, deaths and recoveries by province due to the Coronavirus pandemic in Mozambique.

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.

Daily Covid-19 cases in african countries : daily infections, recoveries and deaths and cumulative cases of infections, recoveries and deaths since the beginning of the pandemic.

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.

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

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.

Overview The dataset contains harmonized indicators created from high-frequency phone surveys collected by the World Bank and partners. The surveys capture the socioeconomic impacts of the COVID-19 pandemic on households and individuals from all developing regions. Data are available for over 90 indicators in 14 topic areas, including education, food security, income, safety nets, and others. For more information, please refer to our Technical Note and Data Dictionary. Unit of Measure Percentages. Aggregation Method: The data is aggregated by Urban/Rural/National and Industry Sector Disclaimer: This harmonized dataset is an ongoing collation and harmonization of COVID-19 high-frequency phone survey (HFPS) data. Harmonization involves redefining indicators and categories so that they are comparable across countries. As a result, even if the names and definitions of indicators appear similar, numbers in this global database might differ slightly from those of each country's publications or dashboard. If you see large discrepancies or other issues, please reach out. Version Notes: COVID-19 Harmonized Household Data Feb 18 • Temporarily suppressed select income, labor, and government assistance indicators collected after wave 2 surveys for harmonization review • Added need for, and access to medical care in multiple countries • Temporarily suppressed select income, labor and government assistance indicators collected after wave 2 surveys for harmonization review Funding Name, Abbreviation, Role: The project received support from the Trust Fund for Statistical Capacity Building III (TFSCB-III). TFSCB-III is funded by the United Kingdom’s Foreign, Commonwealth & Development Office, the Department of Foreign Affairs and Trade of Ireland, and the Governments of Canada and Korea. Other Acknowledgments: This dashboard was created by the Data for Goals (D4G) team and the Regional High-Frequency Phone Survey (HFPS) Focal Points in the EFI Poverty and Equity Global Practice (POV GP), under the guidance of POV GP management, using data collected under the World Bank-wide COVID-19 HFPS initiative. Time Periods: March, 2021

This dataset includes the latest available information on COVID-19 developments impacting the security of aid and health work and operations to help aid agencies meet duty of care obligations to staff and reach people in need.

This dataset contains the number of confirmed cases, recoveries and deaths by Governorate due to the Coronavirus pandemic in Palestine.

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 covid.database.comments@dalberg.com Questions can be submitted to covid.database.questions@dalberg.com www.dalberg.com

The COVID-19 preventative health survey is designed to help policymakers and health researchers better monitor and understand people’s knowledge, attitudes and practices about COVID-19 to improve communications and their response to the pandemic.

Under the leadership of UNDP and DCO, an inter-agency task team developed the UN framework for the immediate socio-economic response to COVID-19 (adopted in April 2020) to govern its response over 12 to 18 months. To measure the UN’s support to the socio-economic response and recovery, UN entities developed a simple monitoring framework with 18 programmatic indicators (endorsed by the UNSDG in July 2020). Lead entities – based on their mandate and comparative advantage – were nominated to lead the development of methodological notes for each indicator and lead the collection of data at the country level. These lead entities reported through the Office of the Resident Coordinators the collective UN results on a quarterly basis through UN Info. All 2020 data was reported by March 2021. This is the UN development system’s first comprehensive attempt at measuring its collective programming contribution and results. These programmatic indicators enabled the UN system to monitor the progress and achievements of UNCT’s collective actions in socio-economic response. In support of the Secretary-General’s call for a "… single, consolidated dashboard to provide up-to-date visibility on [COVID-19] activities and progress across all pillars” all data was published in real time on the COVID-19 data portal, hosted by DCO. The data is disaggregated by geography (rural/urban), sex, age group and at-risk populations -- to measure system-wide results on the socio-economic response to the pandemic, in order to ensure UNDS accountability and transparency for results.

Figures about the evolution of Covid19 in African countries, new infected, recovered and deceased per day and cumulative cases of infected, recovered and deceased.

Raw Global 3W national data visualized in the Global Humanitarian Operational Presence Who, What, Where (3W) Portal as of 23 July 2021 Fields: 'country code' -- example 'UKR' for Ukraine 'sector' -- example 'CCCM' = Camp Coordination / Management 'organization' -- Organization name provided in the field inputs 'type' -- INGO (International NGO), NNGO (National NGO), UN (United Nations), 'Undefined', or 'Other' 'COVID 19' -- 'yes' / 'no' (pertaining to the context) '3W date' -- date of data applicability 'Display Name' -- Organization name - standardized as much as possible Note these intended to be cleaned and standardized, though language variations (such as "Acción Contra el Hambre" / "Action Against Hunger" / "Action Contre La Faim"), and national offices of international organizations (such as "Caritas Internationalis - Nigeria" / "Caritas Internationalis - Switzerland") are preserved. Note also that the names of some organizations working in Protection have been suppressed. Users with questions are encouraged to use the 'Contact the contributor' button below.

This dataset contains excess mortality data for the period covering the 2020 Covid-19 pandemic. The data contains the excess mortality data for all known jurisdictions which publish all-cause mortality data meeting the following criteria: daily, weekly or monthly level of granularity includes equivalent historical data for at least one full year before 2020, and preferably at least five years (2015-2019) includes data up to at least April 1, 2020 Most countries publish mortality data with a longer periodicity (typically quarterly or even annually), a longer publication lag time, or both. This sort of data is not suitable for ongoing analysis during an epidemic and is therefore not included here. "Excess mortality" refers to the difference between deaths from all causes during the pandemic and the historic seasonal average. For many of the jurisdictions shown here, this figure is higher than the official Covid-19 fatalities that are published by national governments each day. While not all of these deaths are necessarily attributable to the disease, it does leave a number of unexplained deaths that suggests that the official figures of deaths attributed may significant undercounts of the pandemic's impact.

Contains data crowdsourced from Venezuelans through the Premise Data mobile application. The survey is presented only once to users and aims to capture current COVID-19 awareness around testing availability and symptoms, and identifies users who have moved to a different state in the last year. More relevant information below: The booklet included HERE goes into more details on how Premise's crowdsourcing works.

The dataset contains estimates of changes in human mobility during the COVID-19 outbreak. These data underly the reports published at https://covid19mm.github.io/. For more details about the data see https://covid19mm.github.io/data.html. If you find the data helpful or you use the data for your research, please cite our work: Pepe, E., Bajardi, P., Gauvin, L., Privitera, F., Lake, B., Cattuto, C., & Tizzoni, M. (2020). COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown. Scientific Data 7, 230 (2020).

Region-level data collected from the official website of Russian Federal Agency Rospotrebnadzor (https://стопкоронавирус.рф/). Dataset exists attributes: data, infected, recovered, died. The dataset contains information of 85 Russian regions.

Country's economic exposure due to COVID-19. Composite indicator based on World Bank's datasets on remittances, food import dependence, primary commodity export dependence, tourism dependence, government indebtedness and foreign currency reserves.

COvid 19 subnational data for iraq

This dataset contains the number of confirmed cases by state due to the Coronavirus pandemic in Venezuela.

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. 13 OCTOBER 2020 UPDATE: The Ethiopia - Subnational Administrative Boundaries dataset has been updated with changes at administrative level 1 and below. However, as the Ethiopia - Subnational Population Statistics dataset has not been updated and no longer matches, this global dataset has not yet been updated. 30 SEPTEMBER 2020 UPDATE: The dataset has been updated to reflect official Brazil P-codes. Users should note that the Brazil P-codes in earlier versions no longer correspond to the current CODs. 17 SEPTEMBER 2020 UPDATE: The dataset has been updated to reflect the new (2020) Paraguay COD-PS projections and the adjusted Paraguay P-codes. Users should note that the Paraguay P-codes in earlier versions no longer correspond to the current CODs. 26 AUGUST 2020 UPDATE: administrative level 1 total population statistics have been added for the following 11 countries: Benin , Djibouti, Liberia, Pakistan, Panama, Philippines, Paraguay, Sierra Leone, Togo, Uruguay, Zimbabwe. Population statistics are now available for 48 of the 63 countries or territories. Data fields: 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

This dataset from original data of COVID-19 statistics collected from the official website of Moscow government.

This data and report examine perceptions and the impact of COVID-19 in 12 countries throughout sub-Saharan Africa. Topics covered include greatest concerns surrounding coronavirus, preventative measures being taken, changes in food market operability and food security, consumer behavior changes, and trust in governments to prevent the spread of coronavirus. This dataset includes data from 10 of the markets. Please contact us for access to data from all markets, the questionnaire, and with any other questions.