As recently pointed by the IOM, the existing models for PI mitigation fall short of providing dynamic decision support which would incorporate "the costs and benefits of intervention" [50] . In this paper, we present a large-scale simulation optimization model which is attempted at filling this gap. The model supports dynamic predictive resource distribution over a network of regions exposed to the pandemic. The model aims to balance both the ongoing and potential outbreak impact, which is measured in terms of morbidity, mortality, and social distancing, translated into the cost of lost productivity and medical expenses. The model was calibrated using historic pandemic data and compared to the myopic strategy, using a sample outbreak in Fla, USA, with over 4 million inhabitants.