Predictive data assimilation through Reduced Order Modeling for epidemics with data uncertainty

Rebollo, T. Chacon; Coronil, D. Franco

In this article, we develop a data assimilation procedure to predict the evolution of epidemics with data uncertainty, with application to the Covid-19 pandemic. We construct a vademecum of solutions by solving the SIR epidemic model for a set of data neighboring the estimated real (or official) ones. A reduced basis is constructed from this vademecum through Proper Orthogonal Decomposition (POD). The reduced POD base is then applied to assimilate the pandemic data (infected, recovered, deceased) during the period in which data are known, by a least squares procedure. The fitted curves are then used to predict the evolution of the pandemic in the next days. Validation tests for Andalusia region (Spain), Italy and Spain show accurate predictions for 7 days that improve as the number of assimilated data increases.