Lily Wang, Professor, Department of Statistics, ISU

Since December 2019, the outbreak of COVID-19 has spread globally within weeks. To efficiently combat COVID-19, it is crucial to have a better understanding of how far the virus will spread and how many lives it will claim. Scientific modeling is an essential tool to answer these questions and ultimately assist in disease prevention, policymaking, and resource allocation. We establish a state-of-art interface between classic mathematical and statistical models to investigate the dynamic pattern of the spread of the disease. We provide both short-term and long-term county-level prediction of the infected/death count for the US by accounting for the control measures, mobility and local features. Utilizing spatiotemporal analysis, our proposed model enhances the dynamics of the epidemiological mechanism, which helps to dissect the spatial and temporal structure of the spreading and predict how this outbreak may unfold through time and space in the future. To assess the uncertainty associated with the prediction, we develop a projection band based on the envelope of the bootstrap forecast paths. Our empirical studies demonstrate the superior performance of the proposed method.