During the past two decades, network scientists have shown that interaction among population members can dramatically influence spreading dynamics. Networked epidemic models explicitly capture interactions among individuals using a contact network where individuals are the nodes, and potential interactions are the edges of the graph. This talk reviews some of the latest network-based modeling approaches for studying the spatio-temporal spread of COVID-19 and the ongoing research at the University of Arizona.
