Local Surveillance of the COVID-19 Outbreak - Liu, Caifen; Xu, Lingfeng; Bai, Yuan; Xu, Xiaoke; Lau, Eric H. Y.; Cowling, Benjamin J.; Du, Zhanwei (Frontiers in Physics, 2022)
Given the worldwide pandemic of the novel coronavirus disease 2019 (COVID-19) and its continuing threat brought by the emergence of virus variants, there are great demands for accurate surveillance and monitoring of outbreaks. A valuable metric for assessing the current risk posed by an outbreak is the time-varying reproduction number (). Several methods have been proposed to estimate using different types of data. We developed a new tool that integrated two commonly used approaches into a unified and user-friendly platform for the estimation of time-varying reproduction numbers. This tool allows users to perform simulations and yield real-time tracking of local epidemic of COVID-19 with an R package.