Infectious disease control in metapopulations with limited resources

Abstract Motivated by unique challenges faced in containing the 2014 Ebola outbreak in West Africa, we develop a framework to dynamically allocate limited resources to several possibly connected populations where the disease transmission is stochastic. We formulate this problem as a stochastic dynamic program. However, as the state and action spaces grow exponentially with the size of the problem, the standard solution techniques do not apply. We propose two solution methodologies along with several benchmark policies. The first approach considers a dynamic one-step look-ahead policy which is equivalent to a nonlinear integer knapsack that scales well with the problem size. The second approach is a modification of a myopic incidence policy found in the literature. In addition to testing the proposed policies in a simulation setting of the optimization framework, we develop a large-scale stochastic simulation for 2014 Ebola outbreak in a case study. We calibrate and validate the stochastic simulation model with real-world data from Sierra Leone. Our results provide insights on efficient prioritization and resource allocation in this setting.