publication . Preprint . 2021

An ensemble model based on early predictors to forecast COVID-19 healthcare demand in France

Paireau, Juliette; Andronico, Alessio; Hozé, Nathanaël; Layan, Maylis; Crepey, Pascal; Roumagnac, Alix; Lavielle, Marc; Boëlle, Pierre-Yves; Cauchemez, Simon;
English
  • Published: 01 Feb 2021
  • Publisher: HAL CCSD
  • Country: France
Abstract
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 19 predictors to anticipate French COVID-19 related healthcare needs from September 7th 2020 to March 6th 2021. We then build an ensemble model by combining the individual forecasts and test this model from March 7th to July 6th 2021. We find that inclusion of early predictors (epidemiological, mobility and meteorological predictors) can halve the root mean square error for 14-day ahead forecasts, with epidemiological and mobility predictors contributing the most to the improvement. On average, the ensemble model is the best or second best model, depending on the evaluation metric. 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.
Subjects
free text keywords: [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie
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