Abstract: We describe the use of data-driven optimization models to inform resource allocation to help detect or mitigate the spread of a virus. One set of models guide preparation for, and response to, an influenza pandemic. In particular, we optimize: the mix of central and regional stockpiles of ventilators, accounting for stochastic peak-week demand; the spatial allocation of antivirals, considering under-insured populations and hard-to-reach locations; and, the spatial allocation of multiple types of vaccines with differing suitability for each prioritized target population. In addition, we discuss rapidly detecting the spread of a cell-phone virus on a contact network of handsets.

Speaker: David Morton is the David A. and Karen Richards Sachs Professor and Chair of Industrial Engineering and Management Sciences at Northwestern University. His research interests include stochastic and large-scale optimization with applications in security, public health, and energy systems. Prior to joining Northwestern, he was on the faculty at the University of Texas at Austin, worked as a Fulbright Research Scholar at Charles University in Prague, and was a National Research Council Postdoctoral Fellow in the Operations Research Department at the Naval Postgraduate School.

This lecture satisfies requirements for CSCI 591: Research Colloquium.