Modeling for Infectious Disease Control in the Context of Information Spread
Abstract: Human behavior and how individuals react to disease can be key drivers of the success (or failure) of disease control polices. Behavioral responses (testing, treatment seeking, social distancing, etc.) are often dependent on the spread of information about the disease, which can have complex dynamics. In this talk, we discuss one way that dynamic compartmental models of disease can tractably simultaneously account for disease and information spread. We consider this method in the context of identifying optimal disease control policies in resource constrained settings, and provide a numerical example of tuberculosis in India.
Short bio: Sze-chuan Suen is a USC Women in Science and Engineering (WISE) Gabilan Assistant Professor in the Daniel J. Epstein Department of Industrial and Systems Engineering at the University of Southern California. She received her PhD in the department of Management Science and Engineering from Stanford University in 2016. Her research interests include developing applied mathematical models to identify epidemiological trends and evaluating health policies to support informed decision-making. Her work in health policy modeling draws from a variety of techniques, including simulation, dynamic systems modeling, Markov decision processes, cost-effectiveness analysis, and decision analysis. Her previous work has examined the optimal management of tuberculosis, HIV, as well as chronic diseases.
Sze was a recipient of the INFORMS Pierskalla Best Paper Award in 2017, a finalist in 2019, and a finalist in the Junior Faculty Forum Paper Competition in 2019. Her work has also been honorably mentioned in the Competition for Best Application Paper in the 2021 IISE Transactions Focus Issue on Operations Engineering and Analytics. Her work has been funded by the National Science Foundation and the National Institutes of Health, and she has participated in a modeling consortium funded by the Bill and Melinda Gates Foundation, among others. Additional information about her and her work is provided on her website: https://szesuen.usc.edu/
Dr. Sze-Chuan Suen