You are here
Theoretical and Applied Data Science Lunch-n-learn - Lily Wang
Presenter: Lily Wang
Title: Spatiotemporal Dynamics, Nowcasting and Forecasting COVID-19 in the United States
Abstract: Since the beginning of the reported cases in 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 prediction modeling is an essential tool to answer these questions and ultimately assist in disease prevention, policymaking, and resource allocation. In the modeling of an epidemic, traditional mathematical models are useful; however, they are deterministic, and only demonstrate the average behavior of the epidemic. In this article, we establish a state-of-art interface between classic mathematical and statistical models to investigate the dynamic pattern of the spread of the disease. Furthermore, we provide real-time short-term and long-term county-level prediction of the infected/death count for the U.S. by accounting for the control measures, environmental effects, socioeconomic factors, health service resources, and demographic conditions. 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 in terms of the accuracy of the short-term forecast. Based on our analysis, disease mappings can easily be implemented to identify high-risk areas. Based on our research findings, we developed multiple R shiny apps embedded into a COVID-19 dashboard, which provides a 7-day forecast and a 4-month forecast of COVID-19 infected and death count at both the county level and state level.
Bio: Dr. Lily Wang is a tenured Associate Professor of Statistics at Iowa State University. She received her Ph.D. in Statistics from Michigan State University in 2007. Her primary areas of research include developing cutting-edge statistical non/semi-parametric methods, statistical learning, methodologies for functional data, imaging data, and spatiotemporal data, survey sampling, high dimensional data, and the application of statistics to problems in economics, neuroimaging, official statistics, and biomedical science. She is an Elected Fellow of Institute of Mathematical Statistics (2020 --), and an Elected Member of the International Statistical Institute (2008 --).
After the presentation, there will be a short time for discussion and questions afterwards.