Data resource profile: COVerAGE-DB: a global demographic database of COVID-19 cases and deaths
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Abstract
Information about pandemic dynamics is crucial to understand the potential impacts on populations, design mitigation strategies and evaluate the efficacy of their implementation. Centralization, standardization and harmonization of data are critical to enable comparisons of the demographic impact of COVID-19 which take into account differences in the age and sex compositions of confirmed infections and deaths. The international data landscape must keep pace with the global march of the pandemic, and researchers must work to triangulate the available data to create comparable measures to monitor and predict its demographic impacts. COVerAGE-DB aims to provide global coverage of key demographic aspects of the COVID-19 pandemic as it unfolds in an up-to-date, transparent and open-access format. COVerAGE-DB offers data with standardized count measures by sex and harmonized age groups, which is a necessary but not sufficient condition to allow comparisons between populations at national and subnational scales. The database is currently under expansion through both the increase in coverage of national and subnational populations and the inclusion of more recent periods as the pandemic continues. At the time of writing, the database contains daily counts of COVID-19 cases, deaths and tests performed, by age and sex, for 108 national and 371 subnational populations around the world, depending on the available data for each source. The date range available for each country or subpopulation varies. In several country series, the database includes the earliest confirmed cases in January 2020. For most populations, the database includes daily time series, beginning from an initial starting date when the data were first released or collected by our team. Figure 1 displays a map of countries included in the database, indicating at least one subnational population from 13 countries. A detailed overview of data availability is given in a searchable table: [https://bit.ly/3kVDrLD]. Availability of national and subnational information on COVID-19 cases, deaths, and tests in the countries included in the database as of 7 January, 2021. Official counts of COVID-19 cases, deaths, and tests are extracted from reports published by official governmental institutions, such as health ministries and statistical offices. Depending on the source, data are collected in a variety of formats, including machine-readable files, pdf tables, html tables, interactive dashboards, press releases, official announcements via Twitter, and in a few instances, from digitized graphics. A full list of data sources is available in a dashboard view [https://bit.ly/2Qg1MxL]. Generally, COVID-19 cases, deaths and tests in age groups are reported as counts, but some sources report data in other metrics (fractions, percentages, ratios) or as summary indicators such as case fatality ratios (CFRs) by age. Reported age intervals vary by source, ranging from single ages to 30-year or greater age bands, and sometimes reported age intervals change over time within sources. Usually data are reported as cross-sectional snapshots of cumulative counts, but some sources give full time series of new cases or deaths, in which case we cumulate counts over time. We also collect standard metadata on each of the sources to capture various characteristics of the collected data, such as the primary collection channels, definitions used and notes on major disruptions or events. An overview of key fields from these metadata is shared as a spreadsheet [https://bit.ly/2FAmKFn]. All source data are entered into standard spreadsheet templates hosted in a central folder on Google Drive. Data entry into the templates is either manual or automatic, depending on the source. R programs collect data from the source templates and compile the merged input database. The merged input file is then subject to a series of automatic validity checks. Initial checks are carried out by the individual responsible for data collection and entry, using an interactive application [https://mpidr.shinyapps.io/cleaning_tracker/]. Data are then harmonized to standard metrics (counts), measures (cases, deaths, tests) and age bands (5- and 10-year age intervals). Harmonization procedures include rescaling to ensure coherence between age distributions and reported total counts. Age group harmonization is done using the penalized composite link model for ungrouping1 which was designed for splitting histograms of count data. Output data also include a file containing selected diagnostics of data quality, such as completeness of age reporting, for each source and date. The complete details on all steps of production are available in the COVerAGE-DB Method Protocol, which is publicly available on the web.2 A table listing which adjustments are applied to each population is available on the project website [https://bit.ly/2E61BSV]. The merged input database, the harmonized output and the data quality files are uploa
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Reported statistics on COVID-19 mortality are problematic for many countries owing to variations in testing access, differential diagnostic capacity and inconsistent certification of COVID-19 as cause of death.
“Reported statistics on COVID-19 mortality are problematic for many countries owing to variations in testing access, differential diagnostic capacity and inconsistent certification of COVID-19 as cause of death.”
From The WHO estimates of excess mortality associated with the COVID-19 pandemic (2023) · cites this paper as [2]
Deaths reported as COVID-19 in the COVerAGE database explain most life-expectancy losses observed.
“We estimated that deaths reported as COVID-19 in the COVerAGE database23 explain most life-expectancy losses observed.”
From Quantifying impacts of the COVID-19 pandemic through life-expectancy losses: a population-level study of 29 countries (2022) · cites this paper as [23]
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- Authors
- Riffe, T., Acosta, E., the COVerAGE-DB team, Acosta, E. J., Manuel Aburto, D., Alburez-Gutierrez, A., Altová, A., Alustiza, U., Basellini, S., Bignami, D., Breton, E., Choi, J., Cimentada, G., De Armas, E., Del Fava, A., Delgado, V., Diaconu, J., Donzowa, C., Dudel, A., Fröhlich, A., Gagnon, M., Garcia-Crisóstomo, V., M Garcia-Guerrero, A., González-Díaz, I., Hecker, D., Eric Koba, M., Kolobova, M., Kühn, M., Lépori, C., Liu, A., Lozer, M., Manea, L., Marey, M., Masum, R., Mogi, C., Monicolle, S., Morwinsky, R., Musizvingoza, M., Myrskylä, M., R Nepomuceno, M., Nickel, N., Nitsche, A., Oksuzyan, S., Oladele, E., Olamijuwon, O., Omodara, S., Ouedraogo, M., Paredes, M., D Pascariu, M., Piriz, R., Pollero, L., Qanni, F., Rehermann, F., Ribeiro, S., Rizzi, F., Rowe, A., R Sarhan, I., Sasson, E., Shomron, J., Shi, R., Silva-Ramirez, C., Strozza, C., Torres, S., Trias-Llimos, F., Uchikoshi, A., van Raalte, P., Vazquez-Castillo, E., A Vilela, M., Ali Waqar, I., Williams, V., & Zarulli
- Affiliations
- Max Planck Institute for Demographic Research (DE)
- Laboratory of Population Health, Max Planck Institute for Demographic Research, Rostock, Germany
- Charles University (CZ)
- Sociétés, Acteurs, Gouvernement en Europe (FR)
- ENSAE Paris (FR)
- Institut national d'études démographiques (FR)
- Universidad de la República de Uruguay (UY)
- University of Southern Denmark (DK)
- Corresponding author
- Riffe, Tim
- Venue
- International Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-04-01
- Volume
- 50
- Issue
- 2
- Pages
- 390-390f
- ISSN
- 0300-5771 (ISSN-L)
- 1464-3685
- PMC
- PMC8128459
- OpenAlex
- W3162206455
- Cited by
- 110
- References
- 25
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 25 · 2023: 13 · 2022: 33 · 2021: 28 · 2020: 1
- FWCI
- 9.57
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Epidemiology (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- University of Southern Denmark Research Portal (University of Southern Denmark) (publishedVersion, cc-by)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
- Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) (submittedVersion, cc-by)
- ePrints Soton (University of Southampton) (submittedVersion, other-oa)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- HAL (submittedVersion, cc-by)
- Archined (submittedVersion, cc-by)
- Publikationen an der Universität Bielefeld (Universität Bielefeld) (submittedVersion, cc-by)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by)
- St Andrews Research Repository (St Andrews Research Repository) (submittedVersion, cc-by)
- Padua Research Archive (University of Padova) (submittedVersion, cc-by)
- Funders
- European Research Council (#716323, #864616, #834103)
- Social Sciences and Humanities Research Council (#756-2019-0768)
- Eunice Kennedy Shriver National Institute of Child Health & Human Development
- National Institutes of Health (#P2CHD047879)
- Fonds de recherche du Québec – Société et culture (#2019-B2Z-257115)
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Keywords
- COVID-19 pandemic, demographic effects, age-sex structure, confirmed cases, COVID-19 mortality, data harmonization, data standardization, open access databases, pandemic monitoring, data triangulation, data quality assessment
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