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Epidemiological Forecasting with Model Reduction of Compartmental Models. Application to the COVID-19 Pandemic

Bakhta, Athmane; Boiveau, Thomas; Maday, Yvon; Mula, Olga (2021), Epidemiological Forecasting with Model Reduction of Compartmental Models. Application to the COVID-19 Pandemic, Biology, 10, 1, p. 22. 10.3390/biology10010022

Type
Article accepté pour publication ou publié
Lien vers un document non conservé dans cette base
https://hal.sorbonne-universite.fr/hal-03117258
Date
2021
Nom de la revue
Biology
Volume
10
Numéro
1
Éditeur
MDPI
Pages
22
Identifiant publication
Métadonnées
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Auteur(s)
Bakhta, Athmane
Service de Thermo-hydraulique et de Mécanique des Fluides [STMF]
Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique [CERMICS]
Boiveau, Thomas
Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique [CERMICS]
Maday, Yvon
Laboratoire Jacques-Louis Lions [LJLL (UMR_7598)]
Mula, Olga cc
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Résumé (EN)
We propose a forecasting method for predicting epidemiological health series on a two-week horizon at regional and interregional resolution. The approach is based on the model orderreduction of parametric compartmental models and is designed to accommodate small amounts ofsanitary data. The efficiency of the method is shown in the case of the prediction of the number ofinfected people and people removed from the collected data, either due to death or recovery, duringthe two pandemic waves of COVID-19 in France, which took place approximately between Februaryand November 2020. Numerical results illustrate the promising potential of the approach
Mots-clés
COVID-19; Reduced basis; Epidemiology; Forecasting; Model reduction