CovidVisualized: Visualized compilation of international updating models’ estimates of COVID-19 pandemic at global and country levels

Résumé fourni par la source

Abstract Objectives: To identify international and periodically updated models of the COVID-19 epidemic, compile and visualize their estimation results, at the global and country levels, and periodically update the compilations. When one or more model predicts an increase in daily cases or infections and deaths in the next one to three months, this can be used as an early alarm for technical advisors to the national and subnational decision-makers to consider suggesting augmentation of preventive interventions. Data description: Five international and periodically updated models of the COVID-19 pandemic were identified, created by: (1) Massachusetts Institute of Technology, Cambridge, (2) Institute for Health Metrics and Evaluation, Seattle, (3) Imperial College, London, (4) Los Alamos National Laboratories, Los Alamos, and (5) University of Southern California, Los Angeles. Estimates of these five identified models were gathered, combined, and graphed at global and two country levels. Canada and Iran were chosen as countries with and without subnational estimates respectively. Compilations of results are periodically updated. Three Github repositories contain the codes and results: “CovidVisualizedGlobal” for the global level, “CovidVisualizedCountry” for a country with subnational estimates, Canada, and “covir2” for a country without subnational estimates, Iran.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.