Optimizing the number of models included in outbreak forecasting ensembles

Abstract

AbstractBased on historical influenza and COVID-19 forecasts, we quantify the relationship between the number of models in an ensemble and its accuracy and introduce an ensemble approach that can outperform the current standard. Our results can assist collaborative forecasting efforts by identifying target participation rates and improving ensemble forecast performance.