Modeling dynamic patterns from COVID-19 data using randomized dynamic mode decomposition in predictive mode and ARIMA

Abstract

The aim of this paper is to gain a deeper understanding of the new Corona virus (Covid-19) dynamics directly from the raw data reported by World Health Organization. We provide a high fidelity mathematical model, fast and computationally inexpensive for modeling the evolution of the pandemic worldwide and we develop an effcient tool for medium term prediction of pandemic dynamics, including infection spreading. We illustrate the excellent behavior of the non-intrusive reduced order model by performing a qualitative analysis.