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

TL;DR

This paper introduces a model reduction-based forecasting method for epidemiological data, specifically applied to COVID-19 in France, demonstrating promising results in short-term predictions with limited data.

Contribution

It presents a novel model reduction approach for compartmental models to improve short-term epidemiological forecasting with sparse data.

Findings

Accurately predicted COVID-19 infection and removal numbers during two pandemic waves.

Effective with limited data and regional resolution.

Shows promising potential in real-world pandemic forecasting.

Abstract

We propose a forecasting method for predicting epidemiological health series on a two-week horizon at the regional and interregional resolution. The approach is based on model order reduction of parametric compartmental models, and is designed to accommodate small amount of sanitary data. The efficiency of the method is shown in the case of the prediction of the number of infected and removed people during the two pandemic waves of COVID-19 in France, which have taken place approximately between February and November 2020. Numerical results illustrate the promising potential of the approach.