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An ensemble model based on early predictors to forecast COVID-19 health care demand in France.
Paireau, Juliette; Andronico, Alessio; Hozé, Nathanaël; Layan, Maylis; Crépey, Pascal; Roumagnac, Alix; Lavielle, Marc; Boëlle, Pierre-Yves; Cauchemez, Simon.
Afiliación
  • Paireau J; Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR 2000, 75015 Paris, France.
  • Andronico A; Direction des Maladies Infectieuses, Santé publique France, 94415 Saint Maurice, France.
  • Hozé N; Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR 2000, 75015 Paris, France.
  • Layan M; Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR 2000, 75015 Paris, France.
  • Crépey P; Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR 2000, 75015 Paris, France.
  • Roumagnac A; Arènes-UMR 6051, RSMS-U 1309, Ecole des Hautes Etudes en Santé Publique, INSERM, CNRS, Université de Rennes, 35043 Rennes, France.
  • Lavielle M; Predict Services, 34170 Castelnau-le-Lez, France.
  • Boëlle PY; INRIA, 91120 Palaiseau, France.
  • Cauchemez S; Centre de Mathématiques Appliquées, Ecole Polytechnique, CNRS, Institut Polytechnique de Paris, 91128 Palaiseau, France.
Proc Natl Acad Sci U S A ; 119(18): e2103302119, 2022 05 03.
Article en En | MEDLINE | ID: mdl-35476520
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-d­ahead 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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: COVID-19 Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans País/Región como asunto: Europa Idioma: En Revista: Proc Natl Acad Sci U S A Año: 2022 Tipo del documento: Article País de afiliación: Francia Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: COVID-19 Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans País/Región como asunto: Europa Idioma: En Revista: Proc Natl Acad Sci U S A Año: 2022 Tipo del documento: Article País de afiliación: Francia Pais de publicación: Estados Unidos