Processing epidemiological data using dynamic mode decomposition method

Abstract

Prediction of disease spreading represents a challenging and actual topic. We propose in this paper a framework for reduced order modeling and forecasting of non-intrusive data with application to epidemiology. We developed a technique based on randomized dynamic mode decomposition (DMD) as a fast and accurate option in model order reduction (ROM). The key innovation consists in prediction of DMD-ROM dynamics using a forecasting sliding window technique. We illustrate the excellent behavior of the proposed methods by performing a qualitative analysis.