|Title:||Modeling to Inform Health Policy: Using Simulation and Optimization for Tuberculosis Control|
|Date:||December 19, 2016 (Monday)|
|Time:||11:00 a.m. - 12:00 n.n.|
|Venue:||Room 121, 1/F, Ho Sin-hang Engineering Building,
The Chinese University of Hong Kong,
|Speaker:||Prof. Sze-chuan Suen
Epstein Department of Industrial & Systems Engineering
University of Southern California
Simulation and optimization frameworks that incorporate individual heterogeneity can be powerful tools to inform health policy decisions, particularly decisions about how to efficiently control infectious diseases in resource-constrained settings. We apply such models to assess policies for control of tuberculosis (TB) in India, where more than two million people have TB.
We first use a microsimulation model to uncover the changing dynamics of drug-resistant (DR) TB. We find that nearly half of new DR TB cases in India are transmission-generated, as opposed to treatment-generated, and we project this proportion to continue to rise, implying that strategies that disrupt DR transmission may provide greater DR prevalence reductions over time.We then incorporate healthcare costs into the simulation and find that both new diagnostics and institutional reform policies that refer patients in informal, private TB clinics to public clinics using approved treatment regimens would both be cost-effective ways of combatting TB in India. However, these institutional reforms should be prioritized if insufficient resources are available to implement both types of policies nationally. Building on the microsimulation results, we use dynamic programming methods to design patient-specific DR TB testing algorithms that can reduce over-testing, reduce costs, and quickly identify DR TB patients. We estimate that the optimal DR TB testing algorithm identified by our analysis will decrease healthcare costs by an average of $4000 per patient by averting downstream transmission.
Sze-chuan Suen is an assistant professor in the Epstein Department of Industrial and Systems Engineering at the University of Southern California.She holds a PhD in Management Science and Engineering from Stanford University.Her research focuses on developing mathematical, economic, and applied operations research approaches to guide health policy decisions, particularly to efficiently control disease in resource-constrained settings.She is interested in developing new and improved methods for model-based policy analyses, designing treatment programs that adapt to patient characteristics, and evaluating the cost-effectiveness of individual- and system-level policies for controlling disease.
Enquiries: Miss Ricola Lo at tel 3943 8440
For more information, please refer to http://www.cse.cuhk.edu.hk/seminar.