At the same time, The IOM report [50] points out several limitations of the MIDAS models, observing that "because of the significant constraints placed on the models . . . the scope of models should be expanded." The IOM recommends "to adapt or develop decision-aid models that can . . . provide real-time feedback . . . and include the costs and benefits of intervention strategies." Our literature review yields a similar observation that most existing approaches focus on assessment of a priori defined strategies, and virtually none of the models are capable of "learning," that is, adapting to changes in the pandemic progress, or even predicting them, to generate dynamic strategies. Such a strategy has the advantage of being developed dynamically, as the pandemic spreads, by selecting a mix of available mitigation options at each decision epoch, based on both the present state of the pandemic and its predicted evolution.