Discovering dynamic models of COVID-19 transmission

Jinwen Liang; Xueliang Zhang; Kai Wang; Manlai Tang; Maozai Tian

doi:10.1111/tbed.14263

Discovering dynamic models of COVID-19 transmission

Transbound Emerg Dis. 2022 Jul;69(4):e64-e70. doi: 10.1111/tbed.14263. Epub 2021 Aug 11.

Authors

Jinwen Liang¹, Xueliang Zhang², Kai Wang², Manlai Tang³, Maozai Tian^{1

2}

Affiliations

¹ Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing, China.
² Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China.
³ Department of Mathematics, College of Engineering, Design & Physical Sciences, Brunel University, London, UK.

Abstract

Existing models about the dynamics of COVID-19 transmission often assume the mechanism of virus transmission and the form of the differential equations. These assumptions are hard to verify. Due to the biases of country-level data, it is inaccurate to construct the global dynamic of COVID-19. This research aims to provide a robust data-driven global model of the transmission dynamics. We apply sparse identification of nonlinear dynamics (SINDy) to model the dynamics of COVID-19 global transmission. One advantage is that we can discover the nonlinear dynamics from data without assumptions in the form of the governing equations. To overcome the problem of biased country-level data on the number of reported cases, we propose a robust global model of the dynamics by using maximin aggregation. Real data analysis shows the efficiency of our model.

Keywords: COVID-19; global transmission; maximin aggregation; sparse identification of nonlinear dynamics (SINDy).

MeSH terms

Animals
COVID-19* / veterinary