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

Autor: Juliette Paireau, Alessio Andronico, Nathanaël Hozé, Maylis Layan, Pascal Crépey, Alix Roumagnac, Marc Lavielle, Pierre-Yves Boëlle, Simon Cauchemez
Přispěvatelé: Modélisation mathématique des maladies infectieuses - Mathematical modelling of Infectious Diseases, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Direction des maladies infectieuses - Infectious Diseases Division [Saint-Maurice], Santé publique France - French National Public Health Agency [Saint-Maurice, France], Centre de Recherches sur l'Action Politique en Europe (ARENES), Université de Rennes (UR)-Institut d'Études Politiques [IEP] - Rennes-École des Hautes Études en Santé Publique [EHESP] (EHESP)-Centre National de la Recherche Scientifique (CNRS), Recherche sur les services et le management en santé (RSMS), Université de Rennes (UR)-École des Hautes Études en Santé Publique [EHESP] (EHESP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Predict Services [Castelnau-le-Lez], Modélisation en pharmacologie de population (XPOP), Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut Polytechnique de Paris (IP Paris), Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), We acknowledge financial support from the Investissement d’Avenir program, the Laboratoire d’Excellence Integrative Biology of Emerging Infectious Diseases program (grant ANR-10-LABX-62- IBEID), Santé publique France, the INCEPTION project (PIA/ANR16-CONV-0005), the European Union’s Horizon 2020 research and innovation program under grants 101003589(RECOVER) and 874735 (VEO), AXA, Groupama, and EMERGEN, ANR-10-LABX-0062,IBEID,Integrative Biology of Emerging Infectious Diseases(2010), ANR-16-CONV-0005,INCEPTION,Institut Convergences pour l'étude de l'Emergence des Pathologies au Travers des Individus et des populatiONs(2016), European Project: 101003589, H2020-SC1-PHE-CORONAVIRUS-2020,RECOVER(2020), European Project: 874735,H2020-SC1-2019-Single-Stage-RTD,VEO(2020), Lassailly-Bondaz, Anne, Integrative Biology of Emerging Infectious Diseases - - IBEID2010 - ANR-10-LABX-0062 - LABX - VALID, Institut Convergences pour l'étude de l'Emergence des Pathologies au Travers des Individus et des populatiONs - - INCEPTION2016 - ANR-16-CONV-0005 - CONV - VALID, Rapid European COVID-19 Emergency Response research - RECOVER - - H2020-SC1-PHE-CORONAVIRUS-20202020-02-14 - 2022-02-13 - 101003589 - VALID, Versatile Emerging infectious disease Observatory - VEO - - H2020-SC1-2019-Single-Stage-RTD2020-01-01 - 2024-12-31 - 874735 - VALID
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Proceedings of the National Academy of Sciences of the United States of America
Proceedings of the National Academy of Sciences of the United States of America, 2022, 119 (18), pp.e2103302119. ⟨10.1073/pnas.2103302119⟩
ISSN: 0027-8424
1091-6490
Popis: 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, to March 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.
Databáze: OpenAIRE
Externí odkaz: