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

Athmane Bakhta; Thomas Boiveau; Yvon Maday; Olga Mula

doi:10.3390/biology10010022

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

Biology (Basel). 2020 Dec 31;10(1):22. doi: 10.3390/biology10010022.

Authors

Athmane Bakhta¹, Thomas Boiveau², Yvon Maday^{3

4}, Olga Mula^{5

6}

Affiliations

¹ Université Paris-Saclay, CEA, Service de Thermo-Hydraulique et de Mécanique des Fluides, 91191 Gif-sur-Yvette, France.
² Sorbonne Université, Institut Carnot Smiles, 75005 Paris, France.
³ Sorbonne Université and Université de Paris, CNRS, Laboratoire Jacques-Louis Lions (LJLL), F-75005 Paris, France.
⁴ Institut Universitaire de France, 75005 Paris, France.
⁵ CEREMADE, CNRS, UMR 7534, Université Paris-Dauphine, PSL University, 75016 Paris, France.
⁶ Inria, Commedia Team, 75012 Paris, France.

Abstract

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 order reduction of parametric compartmental models and is designed to accommodate small amounts of sanitary data. The efficiency of the method is shown in the case of the prediction of the number of infected people and people removed from the collected data, either due to death or recovery, during the two pandemic waves of COVID-19 in France, which took place approximately between February and November 2020. Numerical results illustrate the promising potential of the approach.

Keywords: COVID-19; epidemiology; forecasting; model reduction; reduced basis.