  We study a resource allocation problem for containing an infectious disease
in a metapopulation subject to resource uncertainty. We propose a two-stage
model where the policy maker seeks to allocate resources in both stages where
the second stage resource is random. Instead of a system of nonlinear
differential equations that governs the epidemic trajectories in the
constraints of the optimization model, we use a data-driven functional form to
model the cumulative number of infected individuals. This flexible data-driven
modeling choice allows us to transform the optimization problem to a tractable
mixed integer linear program. Our flexible approach can handle an online
decision making process, where the decision makers update their decisions for
opening treatment units and allocating beds utilizing the new information about
the epidemic progress. We utilize a detailed simulation model, validated by
real data from the 2014 Ebola epidemic in Sierra Leone. Our results show that
our policies produce about 400 fewer number of infected individuals in Sierra
Leone compared to the policies applied during the actual epidemic. We also
provide a detailed comparison of allocation policies generated by our
optimization framework which sheds light on the optimal resource allocation in
different regions.
