In an attempt to address the IOM recommendations, we present a simulation optimization model for developing predictive resource distribution over a network of regional outbreaks. The underlying simulation model mimics the disease and population dynamics of each of the affected regions (Sections 2.1 and 2.2). As the pandemic spreads from region to region, the optimization model distributes mitigation resources, including stockpiles of vaccines and antiviral and administration capacities (Section 2.3). The model seeks to minimize the impact of ongoing outbreaks and the expected impact of potential outbreaks, using measures of morbidity, mortality, and social distancing, translated into the cost of lost productivity and medical expenses. The methodology is calibrated and implemented on a sample outbreak in Fla, USA with over 4M inhabitants (Section 3). The strategy is compared to the reactive myopic policy, which allocates resources from one actual outbreak region to the next, each time trying to cover the entire regional population at risk, regardless of the resource availability. The comparison is done at different levels of vaccine and antiviral availability and administration capacity. We also present a sensitivity analysis for assessing the impact of variability of some critical factors, including: (i) antiviral efficacy, (ii) social distancing conformance, and (iii) CDC response delay.