TSYS School Faculty Trailblazers

Modeling COVID-19 Infections and Recoveries

New research by TSYS School computer scientist Linqiang Ge and his colleagues Yuexin Li, Yang Zhou and Jingyi Zheng of Auburn University, and Xuan Cao of the University of Cincinnati mathematically describes the dynamic of the COVID-19 pandemic, taking immunity, reinfection, and vaccination into account in a way that not only predicts the number of cases, but also monitors the trajectories of changing parameters, such as transmission rate, recovery rate, and the basic reproduction number.  Their study, which appears in a 2021 issue of Frontiers in Artificial Intelligence, finds a significant decrease in the transmission rate in the U.S. after authorities announced a series of orders aiming to prevent the spread of the virus, such as closing non-essential businesses and lockdown restrictions.  Later, as restrictions were gradually lifted, their analysis detects a new surge of infection as transmission rates show increasing trends in some states.  As Ge stated to Turner Business, “using our epidemiology models, people can track, timely monitor, and predict the COVID-19 pandemic with precision.”  The researchers validated their models using both national- and state-level data, and the resulting relative prediction errors for the infected group and recovered group are generally less than 0.5 percent.  “We also simulate the long-term development of the pandemic based on our proposed models to explore when the crisis will end under certain conditions,” Ge added.