Nonpharmaceutical intervention (NPI) includes social distancing and travel restrictions. We adopted a CDC guidance [51] , which establishes five categories of pandemic severity and recommends quarantine and closure options according to the category. The categories are determined based on the value of the case fatality ratio (CFR), the proportion of fatalities in the total infected population. For CFR values lower than 0.1% (Category 1), voluntary at-home isolation of infected cases is implemented. For CFR values Influenza Research and Treatment 5 between 0.1% and 1.0% (Categories 2 and 3), in addition to at-home isolation, the following measures are recommended: (i) voluntary quarantine of household members of infected cases and (ii) child and adult social distancing. For CFR values exceeding 1.0% (Categories 4 and 5), all the above measures are implemented. As the effectiveness of social distancing is affected by some of the behavioral factors listed above [54] , we assume a certain social distancing conformance level. Travel restrictions considered in the model included regional air and land border control for infected travelers. Figure 2 , the optimization model is invoked at the beginning of every nth new regional outbreak epoch (n = 1, 2, . . .), starting from the initial outbreak region (n = 1). The objective of the model is to allocate some of the available mitigation resources to the new outbreak region (actual distribution) while reserving the rest of the quantities for potential outbreak regions (virtual distribution). By doing so, the model seeks to progressively minimize the impact of ongoing outbreaks and the expected impact of potential outbreaks, spreading from the ongoing locations. Mitigation resources can include stockpiles of vaccines and antivirals, administration capacity, hospital beds, medical supplies, and social distancing enforcement resources, among others. The predictive mechanism of the optimization model is based on a set of regression equations obtained using single-region simulation models. In what follows, we present the construction of the optimization model and explain the solution algorithm for the overall simulation-based optimization methodology.