Spatial Resource Allocation for Emerging Epidemics: A Comparison of Greedy, Myopic, and Dynamic Policies

Abstract

Rapidly evolving infectious disease epidemics, such as the 2014 West African Ebola outbreak, pose significant health threats and present challenges to the global health community because of their heterogeneous geographic spread. Policy makers must allocate limited intervention resources quickly, in anticipation of where the outbreak is moving next. We develop a two-stage model for optimizing when and where to assign Ebola treatment units across geographic regions during the outbreak’s early phases. The first stage employs a novel dynamic transmission model to forecast the occurrence of new cases at the region level, capturing connectivity among regions. We introduce an empirically estimated coefficient for behavioral adaptation to changing epidemic conditions. The second stage compares four approaches to allocate units across affected regions: (i) a heuristic based on observed cases, (ii) a greedy policy that prioritizes regions based on the reproductive number, (iii) a myopic linear program that allocates resources in the next period based on an iterative estimation–optimization approach coupled with the underlying epidemic model, and (iv) an approximate dynamic programming algorithm that optimizes over all future periods. After testing the allocation schemes under different budgets and time periods, we find that the myopic policy performs best, even when limited data are available. Our methodology could be generalized to other disease outbreaks, including the Zika virus, and other interventions. The online appendix is available at https://doi.org/10.1287/msom.2017.0681 .