COVID-19 Open-Data a global-scale spatially granular meta-dataset for coronavirus disease

Oscar Wahltinez, Aurora Cheung, Ruth Alcantara,Donny Cheung,Mayank Daswani, Anthony Erlinger, Matt Lee,Pranali Yawalkar, Paula Lê, Ofir Picazo Navarro, Michael P. Brenner, Kevin Murphy


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This paper introduces the COVID-19 Open Dataset (COD), available at . A static copy is of the dataset is also available at . This is a very large “meta-dataset” of COVID-related data, containing epidemiological information, from 22,579 unique locations within 232 different countries and independent territories. For 62 of these countries we have state-level data, and for 23 of these countries we have county-level data. For 15 countries, COD includes cases and deaths stratified by age or sex. COD also contains information on hospitalizations, vaccinations, and other relevant factors such as mobility, non-pharmaceutical interventions and static demographic attributes. Each location is tagged with a unique identifier so that these different types of information can be easily combined. The data is automatically extracted from 121 different authoritative sources, using scalable open source software. This paper describes the format and construction of the dataset, and includes a preliminary statistical analysis of its content, revealing some interesting patterns.
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Key words
Health policy,Influenza virus,Science,Humanities and Social Sciences,multidisciplinary
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