A worldwide epidemiological database for COVID-19 at fine-grained spatial resolution.

Area of Science:

• Epidemiology
• Public Health
• Data Science

Background:

• The COVID-19 pandemic necessitates robust, accessible data for global analysis.
• Existing datasets often lack granular detail or comprehensive integration of related variables.

Purpose of the Study:

• To create a unified, high-resolution global database of COVID-19 metrics.
• To facilitate research by integrating epidemiological data with policy, environmental, and mobility information.

Main Methods:

• Automated, open-source data acquisition pipeline for daily time-series.
• Integration of data for over 230 countries, 760 regions, and 12,000 administrative divisions.
• Provision of data in CSV and SQLite formats, including hourly updates and vintage data for reproducibility.

Main Results:

• A comprehensive daily time-series database of COVID-19 cases, deaths, recoveries, tests, vaccinations, and hospitalizations.
• Geographical entities linked with identifiers for integration with hydrometeorological, geospatial, and mobility data.
• Inclusion of national and sub-national policy measures.

Conclusions:

• The database enables large-scale, high-resolution pandemic research.
• Facilitates the study of within-country variations, nonpharmaceutical interventions, and environmental factors.
• Ensures research reproducibility through vintage data and unified access.