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.

Female carriers of a BRCA1 or BRCA2 genetic mutation face significantly elevated risks of cancer, with 45%-65% of women developing breast cancer and 15%-39% developing ovarian cancer in their lifetimes. Prophylactic surgery options to reduce cancer risk include a bilateral mastectomy (BM), bilateral salpingo-oophorectomy (BSO), or both surgeries. No comprehensive model providing recommendations at which age to perform the surgeries to optimize quality-adjusted life years (QALYs) exists. Using available clinical data, we develop a Markov decision process model of a mutation carrier's health states and corresponding transitions, including age-dependent breast and ovarian cancer risk, distribution of each cancer subtype and stage, and mortality. We convert the problem to a linear program to solve for the optimal surgery sequence that maximizes the carrier's expected lifetime QALYs under varying assumptions about individual patient preferences on postsurgery quality of life, fertility considerations, advances in cancer screening or treatment, and others. Baseline results demonstrate that a QALY-maximizing sequence recommends BM between ages 30 and 60 and BSO after age 40. Surgeries are recommended later for BRCA2 mutation carriers, given their lower risk for both cancers compared to BRCA1 mutation carriers. We derive structural properties from the model and show that when a carrier has already undergone one surgery, there exists an optimal control limit beyond which performing the other surgery is always QALY maximizing.