Research on the propagation model of COVID-19 based on virus dynamics

Abstract

At the end of 2019, a new kind of coronavirus spread in Wuhan city of Hubei province and other places, seriously endangering people's health. Scientific prediction of the spread trend of the novel coronavirus makes a big difference in epidemic prevention, treatment, and relevant health decisions. The COVID-19 transmission model based on virus dynamics was established. We propose a data-driven dynamic modeling method for infectious disease transmission, which is a sparse identification method for nonlinear dynamic models. Sparse regression and parameter identification are used to accurately find the control equation from the potential dynamic models to simulate the dynamic transmission process of the novel coronavirus in Wuhan at the end of 2019. Through the experiment, we get the results that the model can well describe the spread of COVID-19 in Wuhan and also prove that the model is practical and can be extended to the prediction of related epidemic situations.