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

• DOI: 10.1073/pnas.2103302119
• Publication Date: 2022-04-27T18:23:15Z
• Authors: Juliette Paireau; Alessio Andronico; Nathanaël Hozé; Maylis Layan; Pascal Crépey; Alix Roumagnac; Marc Lavielle; Pierre-Yves Boëlle; Simon Cauchemez
• Keywords: MESH: Health Services Needs and Demand; MESH: Pandemics; Health Services Needs and Demand; 0303 health sciences; MESH: Humans; MESH: Delivery of Health Care; 610; COVID-19; MESH: Retrospective Studies; forecasting; Biological Sciences; 3. Good health; MESH: France; 03 medical and health sciences; [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie; MESH: COVID-19; Humans; [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie; ensemble model; France; Delivery of Health Care; Pandemics; Retrospective Studies

Significance The COVID-19 pandemic is inducing significant stress on health care structures, which can be quickly saturated with negative consequences for patients. As hospitalization comes late in the infection history of a patient, early predictors—such as the number of cases, mobility, climate, and vaccine coverage—could improve forecasts of health care demand. Predictive models taken individually have their pros and cons, and it is advantageous to combine the predictions in an ensemble model. Here, we design an ensemble that combines several models to anticipate French COVID-19 health care needs up to 14 days ahead. We retrospectively test this model, identify the best predictors of the growth rate of hospital admissions, and propose a promising approach to facilitate the planning of hospital activity.