Mathematical models of infectious diseases -- Consequences of underlying assumption
Mathematical models have been used to study various disease transmission dynamics and control for epidemics. Many of these studies are based on SEIR- types of compartmental models with exponentially distributed stage durations. We examine the underlying assumptions made in some of these models and present examples to illustrate the potential issues associated with these assumptions in terms of model evaluations of control and intervention strategies.
Biography: Zhilan Feng studied mathematics at Jilin and Arizona State Universities, where she was a doctoral student of Horst Thieme. She was a post-doctoral and visiting fellow with Carlos Castillo-Chavez and Simon Levin at Cornell and Princeton Universities, respectively, before joining the faculty in the Department of Mathematics at Purdue University, where she became full professor in 2005. She is currently a program director for the Mathematical Biology program in the Division of Mathematical Sciences at the National Science Foundation. She was elected a Fellow of the American Mathematical Society in 2021. Her research includes mathematical modeling of ecology and epidemiology using ordinary, partial, and integro-differential equations. Many of her research projects had been partially supported by grants from NSF, CDC, James S. McDonnell Foundation, and Showalter Trust. She has supervised 16 Ph.D. students at Purdue University. She has co-authored three books and published more than 100 papers on mathematical biology and applied mathematics. She served as an editor for Journal of Theoretical Biology, Mathematical Biosciences, SIAM Journal of Applied Mathematics, and Journal of Biological Dynamics. She is currently a member of the Board of Directors of SMB.