Updated
Live
| Dataset date: January 09, 2005-May 17, 2020
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.
Updated
11 May 2020
| Dataset date: April 24, 2020-April 24, 2020
Public health and social measures (PHSMs) are measures or actions by individuals, institutions, communities, local and national governments and international bodies to slow or stop the spread of an infectious disease, such as COVID-19.
Since the start of the COVID-19 pandemic, a number of organizations have begun tracking implementation of PHSMs around the world, using different data collection methods, database designs and classification schemes. A unique collaboration between WHO, the London School of Hygiene and Tropical Medicine, ACAPS, University of Oxford, Global Public Health Intelligence Network, US Centers for Disease Control and Prevention and the Complexity Science Hub Vienna has brought these datasets together, using a common taxonomy and structure, into a single, open-content dataset for public use.
Updated
Live
| Dataset date: January 01, 2020-August 17, 2022
Aid funding related to the global COVID-19 pandemic, as published via the International Aid Transparency Initiative. This data is a result of a collaboration between the Centre for Humanitarian Data and USAID, and is the basis for the IATI COVID-19 Funding Dashboard and related data story.
For more information about data licensing, see https://data.humdata.org/viz-iati-c19-dashboard/about#licensing
Updated
27 September 2021
| Dataset date: January 01, 2020-August 20, 2021
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.
Updated
8 September 2021
| Dataset date: January 01, 2017-July 31, 2021
These datasets contain information on violent and threatening incidents affecting aid operations, civilians, education, health care, refugees and IDPs to ensure staff safety and better response outcomes.
Updated
27 March 2022
| Dataset date: March 09, 2020-March 18, 2022
Subnational data about Covid19 in Burkina Faso - Infected (new cases, gender), Deceased, Recovered.
NEW (!) : VACCINATION DATA PER REGION (1st & 2nd dose)
Type of vaccine : AstraZeneca ; Johnson&Johnson
Updated
4 August 2021
| Dataset date: August 01, 2020-August 17, 2022
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.
Updated
12 October 2020
| Dataset date: September 12, 2020-September 12, 2020
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.
Updated
23 April 2021
| Dataset date: October 14, 2020-March 14, 2021
This data set includes vaccine-related rumour content from Internew's Rooted in Trust Project. The data set contains instances of COVID-19 related rumours shared on social media and in person with Internews in Afghanistan, Lebanon, Philippines, Colombia, Sudan, Mali, and CAR in 2020 and 2021.
Updated
2 December 2020
| Dataset date: September 09, 2020-September 09, 2020
The objective of the dataset is to provide information that enables decision makers to better direct their efforts in addressing the wider effects of the COVID-19 pandemic. The dataset will track secondary impacts across a wide range of relevant themes: economy, health, migration, education to name a few.
A set of around 80 impact indicators anticipated to be impacted by COVID-19 have been identified and organised across 4 pillars and 13 thematic blocks. Additionally, a set of around 25 pre-COVID-19 baseline indicators have been selected for each pillar.
The data collection is conducted on a country-level and identifies the secondary impacts the COVID- 19 pandemic is having in more than 190 countries. Data comes from a range of available sources, including international organisations, research centres, and media analysis.
Note: These are the preliminary results of the data collection on secondary impacts. This dataset is currently in the beta-testing phase, we will keep improving and updating in the coming weeks.
Updated
2 September 2020
| Dataset date: August 31, 2020-August 31, 2020
This dataset contains scores for humanitarian access constraints into country, constraints within country, impacts the constraints have led to as well as the mitigation strategies in place to limit the impact.
The scores have the following interpretations:
0 = NA,
1 = No or open,
2 = partially open/closed,
3 = Yes or closed
Updated
26 May 2020
| Dataset date: May 26, 2020-May 26, 2020
The COVID-19 Humanitarian Exemptions to Government Measures Dataset puts together all the measures implemented by governments worldwide in response to the Coronavirus pandemic.
Updated
5 February 2021
| Dataset date: November 20, 2020-December 14, 2020
Democratic Republic of the Congo Coronavirus(COVID-19) Subnational Cases per province. The dataset has been made available on the Food security cluster website. It is made available via a collaboration between multiple agencies including: DR Congo Ministry of Health, World Health Organisation, UNICEF, JICA, CDC, PATH and IOM
Updated
26 April 2022
| Dataset date: July 10, 2019-April 27, 2021
This dataset contains the results of a household survey to evaluate the impact of an unconditional cash transfer on child labour and other child and household outcomes amongst cocoa farmers in Ghana. Baseline (2019) and follow-up (2021) data was collected from a sample of 644 cocoa growing households in Ahafo and Eastern regions. All recipient households were members of certified cooperative.
The study was set up as a Randomized Control Trial - a randomly selected sub-set of these farmers received 6 months of unconditional cash payments between the baseline and follow-up survey.
Updated
20 January 2021
| Dataset date: May 17, 2020-August 17, 2022
Figures about the evolution of Covid19 in African countries, new infected, recovered and deceased per day and cumulative cases of infected, recovered and deceased.
