Simulation optimization for allocation of epidemic-control resources

We consider the problem of resource allocation (RA) in the control of epidemics where a fixed budget is allocated among competing healthcare interventions to achieve the best health benefits, and propose a simulation-optimization framework to address a general form of the problem. While traditional approaches to the epidemic RA problem suffer from restrictive assumptions to facilitate exact analytical solutions, a simulation-based technique relaxes such assumptions and provides a more realistic representation of the epidemic. Coupling the simulation model with optimization techniques enables us to analyze the behavior of RA outcomes with regard to different investment strategies and seek optimal allocations. We discuss implementation steps and illustrate our approach for an RA problem in the control of influenza pandemic with several interacting healthcare interventions.