9:50am Room B - The Role of Data Science in COVID-19 Projection
The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of public policymaking. Predicting the severity and speed of transmission of COVID-19 is crucial to resource management and developing strategies to deal with this epidemic. Despite the usefulness, modeling and forecasting the spread of COVID-19 remains a challenge. In this talk, we selectively review models for epidemic forecasting, especially in COVID-19 projection, and illustrate how these models could be connected to each other. We lay out the challenges of COVID-19 forecasting from data collection, curation, and validation to the limitations of models as well as the uncertainty of the forecast. Finally, we discuss some data science practices that are crucial to more robust and accurate epidemic forecasting.
Dr. Lily Wang
Department of Statistics
Iowa State University
Dr. Lily Wang is a tenured Professor of Statistics at Iowa State University. She received her Ph.D. in Statistics from Michigan State University in 2007. Prior to joining Iowa State in 2014, she was a tenured Assistant/Associate Professor in the Department of Statistics at the University of Georgia (2007-2014). Her primary areas of research include data science, nonparametric regression, methodologies for functional data, imaging data, and spatiotemporal data, and survey sampling. Dr. Wang is an Elected Fellow of the Institute of Mathematical Statistics (2020-), an Elected Member of the International Statistical Institute (2008-), and was the recipient of Senior Research Fellowship from American Statistical Association/National Science Foundation/Bureau of Labor Statistics in 2010. Dr. Wang is currently serving on the editorial board of Journal of the Royal Statistical Society, Series B, Journal of Nonparametric Statistics, and Statistical Analysis and Data Mining.