In the testbed scenario, the DPO strategy on average outperformed the myopic policy. As opposed to the DPO strategy, the myopic policy is reactive, rather than predictive, as it allocates resources regardless of the remaining availability and the overall cross-regional pandemic status. In contrast, the DPO model distributes resources trying to balance the impact of actual outbreaks and the expected impact of potential outbreaks. It does so by exploiting regionspecific effectiveness of mitigation resources and dynamic reassessment of pandemic spread probabilities, using a set of regression submodels. Hence, we believe that in scenarios involving regions with a more heterogeneous demographics, the DPO policy will likely to perform even better and with less variability than the myopic strategy. We also note that the difference in the model performance was particularly noticeable at lower levels of resource availability, which is in accordance with a higher marginal utility of additional availability at that levels. We thus believe that the DPO model can be particularly useful in scenarios with very limited resources.