The policies produced by this framework are optimal in that they minimize the expected cost of the epidemic and adaptive in that the optimal policy changes as a function of the state of the epidemic and the degree of uncertainty in underlying model parameters. Using extensive simulation studies we compare the distribution of costs accrued under adaptive intervention to those arising from non-adaptive policies in a variety of scenarios. Our studies show that adaptive policies perform similarly to nonadaptive policies based on perfect parameter estimates, and significantly better than nonadaptive policies based on imperfect parameter estimates. Additionally, we show that adaptive online estimation affords the method some robustness to model misspecification. These results further demonstrate the importance of accounting for such underlying uncertainties in dynamic settings and indicate the utility of adaptive policies in settings where perfect estimates and a true model do not exist. All computational methods used herein have been made freely available through the amei (Adaptive Management of Epidemiological Interventions) R package [11] .