Assessing the Impact of Government Interventions on the Spread of COVID-19 with Dynamic Epidemic Models: A case study of Texas

Abstract

COVID-19 has been rapidly spreading and causing hundreds of thousands of casualties across the world. This disease originated in Wuhan, China in 2019, and it has since proliferated into a pandemic, causing the public's daily lives to change drastically around the world. Despite the evolution of COVID-19, remarkably little is known about the efficacy of proposed protective safety orders. Thus, our paper is intended to study the effectiveness of the local and state government restrictions and closures in limiting the spread of COVID-19. To mathematically model the spread of COVID-19, we propose a time-dependent SIR model together with Lasso to monitor the trajectories of the transmission and recover rates in relation to the government closures and restrictions, and further predict the number of cases. To validate our model, we conduct both simulation and state level data analysis. Since the government orders vary among different states, we use Texas as an example state to illustrate our algorithm for assessing the government intervention. Our one-day prediction error for the confirmed cases is around 2.47% and less than 1% for the recovered cases. We also find that there are many intervention methods that corresponded to changes in infection rate and recovery rate of the population.