Discovering dynamic models of COVID‐19 transmission

Abstract

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.