| Summary: | Andrés Uribe-Sánchez, Alex Savachkin1Department of Industrial and Management Systems Engineering University of South Florida, Tampa, FL 33620, USAAbstract: As recently pointed out by the Institute of Medicine, the existing pandemic ­containment and mitigation models lack the dynamic decision support capabilities. We ­present two simulation-based optimization models for developing dynamic predictive resource ­distribution strategies for cross-regional pandemic outbreaks. In both models, the underlying simulation mimics the disease and population dynamics of the affected regions. The quantity-based optimization model generates a progressive allocation of limited quantities of mitigation resources, including vaccines, antiviral, administration capacities, and social distancing enforcement resources. The budget-based optimization model strives instead allocating a total resource budget. Both models seek to minimize the impact of ongoing outbreaks and the expected impact of potential outbreaks. The models incorporate measures of morbidity, mortality, and social distancing, translated into the societal and economic costs of lost productivity and medical expenses. The models were calibrated using historic pandemic data and implemented on a sample outbreak in Florida, with over four million inhabitants. The quantity-based model was found to be inferior to the budget-based model, which was advantageous in its ability to balance the varying relative cost and effectiveness of individual resources. The models are intended to assist public health policy makers in developing effective distribution policies for mitigation of influenza pandemics.Keywords: pandemic, mitigation, distribution, dynamic, resources, budget
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