TY - JOUR
T1 - An ensemble model based on early predictors to forecast COVID-19 health care demand in France
AU - Paireau, Juliette
AU - Andronico, Alessio
AU - Hozé, Nathanaël
AU - Layan, Maylis
AU - Crépey, Pascal
AU - Roumagnac, Alix
AU - Lavielle, Marc
AU - Böelle, Pierre Yves
AU - Cauchemez, Simon
N1 - Publisher Copyright:
© 2022 the Author(s).
PY - 2022/5/3
Y1 - 2022/5/3
N2 - Short-term forecasting of the COVID-19 pandemic is required to facilitate the planning of COVID-19 health care demand in hospitals. Here, we evaluate the performance of 12 individual models and 19 predictors to anticipate French COVID-19-related health care needs from September 7, 2020, toMarch 6, 2021.We then build an ensemble model by combining the individual forecasts and retrospectively test this model from March 7, 2021, to July 6, 2021. We find that the inclusion of early predictors (epidemiological, mobility, and meteorological predictors) can halve the rms error for 14-d-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 that avenues for future improvements can be identified.
AB - Short-term forecasting of the COVID-19 pandemic is required to facilitate the planning of COVID-19 health care demand in hospitals. Here, we evaluate the performance of 12 individual models and 19 predictors to anticipate French COVID-19-related health care needs from September 7, 2020, toMarch 6, 2021.We then build an ensemble model by combining the individual forecasts and retrospectively test this model from March 7, 2021, to July 6, 2021. We find that the inclusion of early predictors (epidemiological, mobility, and meteorological predictors) can halve the rms error for 14-d-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 that avenues for future improvements can be identified.
KW - COVID-19
KW - ensemble model
KW - forecasting
U2 - 10.1073/pnas.2103302119
DO - 10.1073/pnas.2103302119
M3 - Article
C2 - 35476520
AN - SCOPUS:85128982852
SN - 0027-8424
VL - 119
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 18
M1 - e2103302119
ER -