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Employing mathematical modeling and analytical optimization techniques, traditional approaches to the resource-allocation (RA) problem for control of epidemics often suffer from unrealistic assumptions, such as linear scaling of costs and benefits, independence of populations, and positing that the epidemic is static over time. Analytical solutions to more realistic models, on the other hand, are often difficult or impossible to derive even for simple cases, which restricts application of such models. We develop an agent-based simulation model of epidemics, and apply response-surface methodology to seek an optimum for the RA output in an iterative procedure. Validation is demonstrated through comparison of the results with the mathematical solution in an RA example for which the analytical solution is known. We apply the proposed approach to a more complicated RA problem in which a number of previous restricting assumptions are relaxed.