2022/01/28 by Evan L Ray, Logan Brooks, Ray, Evan L. +27 · 1 citation
Decision Sciences · Mathematics · Medicine · #COVID-19 epidemiological studies #Data-Driven Disease Surveillance #FOS: Computer and information sciences #Forecasting Techniques and Applications #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.2201.12387
openalex publication_date 2022/01/28 · openalex created_date 2022/04/03 · openalex updated_date 2026/08/01
The U.S. COVID-19 Forecast Hub aggregates forecasts of the short-term burden of COVID-19 in the United States from many contributing teams. We study methods for building an ensemble that combines forecasts from these teams. These experiments have informed the ensemble methods used by the Hub. To be most useful to policy makers, ensemble forecasts must have stable performance in the presence of two key characteristics of the component forecasts: (1) occasional misalignment with the reported data, and (2) instability in the relative performance of component forecasters over time. Our results indicate that in the presence of these challenges, an untrained and robust approach to ensembling using an equally weighted median of all component forecasts is a good choice to support public health decision makers. In settings where some contributing forecasters have a stable record of good performance, trained ensembles that give those forecasters higher weight can also be helpful.