Estimating Parameters of Two-Level Individual-Level Models of the COVID-19 Epidemic Using Ensemble Learning Classifiers - Liu, Zeyi; Deardon, Rob; Fu, Yanghui; Ferdous, Tahsin; Ware, Tony; Cheng, Qing (Frontiers in Physics, 2021)
The ongoing COVID-19 pandemic has led to a serious health crisis, and information obtained from disease transmission models fitted to observed data is needed to inform containment strategies. As the transmission of virus varies from city to city in different countries, we use a two-level individual-level model to analyze the spatiotemporal SARS-CoV-2 spread. However, inference procedures such as Bayesian Markov chain Monte Carlo, which is commonly used to estimate parameters of ILMs, are computationally expensive. In this study, we use trained ensemble learning classifiers to estimate the parameters of two-level ILMs and show that the fitted ILMs can successfully capture the virus transmission among Wuhan and 16 other cities in Hubei province, China.