title: Optimization methods for decision making in disease prevention and epidemic control.
abstract: This paper investigates problems of disease prevention and epidemic control (DPEC), in which we optimize two sets of decisions: (i) vaccinating individuals and (ii) closing locations, given respective budgets with the goal of minimizing the expected number of infected individuals after intervention. The spread of diseases is inherently stochastic due to the uncertainty about disease transmission and human interaction. We use a bipartite graph to represent individuals' propensities of visiting a set of location, and formulate two integer nonlinear programming models to optimize choices of individuals to vaccinate and locations to close. Our first model assumes that if a location is closed, its visitors stay in a safe location and will not visit other locations. Our second model incorporates compensatory behavior by assuming multiple behavioral groups, always visiting the most preferred locations that remain open. The paper develops algorithms based on a greedy strategy, dynamic programming, and integer programming, and compares the computational efficacy and solution quality. We test problem instances derived from daily behavior patterns of 100 randomly chosen individuals (corresponding to 195 locations) in Portland, Oregon, and provide policy insights regarding the use of the two DPEC models.
authors.affiliation: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Yan
authors.initials: Y
authors.lastname: Deng
authors.forename: Siqian
authors.initials: S
authors.lastname: Shen
authors.forename: Yevgeniy
authors.initials: Y
authors.lastname: Vorobeychik
mesh_terms: D003657:Decision Making; D003661:Decision Support Techniques; D018562:Disease Transmission, Infectious / Q000517:prevention & control*; D058872:Epidemics / Q000517:prevention & control*; D006801:Humans; D013269:Stochastic Processes
keywords: 0–1 Knapsack problem; Compensatory behavior modeling; Disease prevention and intervention; Dynamic programming; Dynamic/static disease control