CAREER: Big Computation and the Management of Emerging Infectious Diseases

Emerging infectious diseases (EIDs) account for more than 25% of global disease burden and more than 32% of global deaths. Current EIDs like Middle East Respiratory Syndrome Coronavirus (MERS) and antibiotic-resistant superbugs have the potential to make devastating impacts on public health. The methodologies under development in this project can be used to translate real-time data on EIDs into recommendations about where, when, and to whom to apply interventions so as to minimize negative impacts of the disease while reducing overall resource consumption. Furthermore, these recommendations are designed to be immediately interpretable in a subject matter context, thereby empowering decision makers to incorporate information from complex and heterogeneous data streams into disease management. Application of these methodologies has the potential to reduce mortality and morbidity at lower cost than existing management plans. Furthermore, models underpinning intervention recommendations will generate new knowledge about EID dynamics. This research project aims to make fundamental contributions to online sequential decision making and to create a new statistical framework for data-driven management of EIDs. We conceptualize the EID as spreading across a finite set of locations, which might be physical locations in space or nodes in a network. An allocation strategy formalizes management of an EID and is represented by a sequence of functions, one per intervention decision, that map up-to-date information on an EID to a subset of locations recommended for treatment. An optimal allocation strategy maximizes some mean utility function over the duration of the EID. Construction of an optimal allocation strategy from data on an EID is challenging because: (i) the number of allocations is exponential in the number of locations; (ii) estimation and management must occur simultaneously; (iii) spatial proximity induces causal interference; and (iv) an allocation strategy must be interpretable to subject matter experts. We integrate ideas from statistics, computer science, optimization, and disease ecology to overcome these challenges. We combine simulation-optimization with policy-search algorithms to construct an online estimator of the optimal allocation strategy; this strategy trades off exploring allocation choices that improve estimates of disease dynamics with exploiting current estimated dynamics to immediately slow spread of the EID. We show that the treatment allocation problem can be recast as an infinite-dimensional bandit problem. We leverage this connection to derive estimation algorithms that scale to very large allocation problems and are amenable to theoretical study. We combine our policy-search and bandit-based estimators with a novel class of allocation strategies that can be expressed as a sequence of if-then statements that are immediately interpretable to subject-matter experts and can be readily adjusted based on expert judgment. We derive a non-parametric lower bound on the approximation error of an estimated allocation strategy within this class; this bound is used to perform goodness-of-fit tests for the estimated optimal allocation strategy.