An ensemble model based on early predictors to forecast COVID-19 healthcare demand in France
Résumé
Short-term forecasting of the COVID-19 pandemic is required to facilitate the planning of COVID-19 healthcare demand in hospitals. Here, we evaluate the performance of 12 individual models and 22 predictors to anticipate French COVID-19 related healthcare needs from September 7th 2020 to January 7th 2021, and build an ensemble model that outperforms all individual models. We find that inclusion of early predictors (epidemiological, mobility and meteorological predictors) can halve the relative error for 14-day ahead forecasts, with epidemiological and mobility predictors contributing the most to the improvement. Our approach facilitates the comparison and benchmarking of competing models through their integration in a coherent analytical framework, ensuring avenues for future improvements can be identified.
