Initial uncertainty regarding parameter values is expressed in the form of vague/noninformative prior distributions, as specified in the Methods. The choice of prior distributions in Bayesian models is of fundamental importance, and other possible choices are mentioned in the Discussion section. At each time step, the state of the epidemic is advanced one time step using the same ''true'' parameter values used in the previous section. Intervention strategies, however, are calculated based on the current parameter estimates. Figures 6 and 7 show the distributions of susceptible, infected, recovered, and vaccinated individuals, and total accumulated costs for repeated simulation of the epidemic under adaptive management. These dynamics can be compared to those in The tighter bound about a smaller mean is due to the ability of the adaptive strategies to methodically diminish the vaccination campaign as a function of the epidemic state. This can be seen in Figures 8 and 9 , which display the distributions of implemented vaccination strategies for each time step during the course of adaptive management. In the average case (dashed line), the maximum policy is enacted for 3 time steps, followed by a round of 20% vaccination. The uncertainty surrounding the implemented strategies indicates the degree to which the the adaptive policies are adjusted in light of data. In epidemics associated with the upper 97.5 percentile of vaccination strategies (top solid line in Figures 8 and 9 ), the adaptive policy calls for 4 rounds of maximum vaccination followed by a round of 20% vaccination, followed by a final round of 5% vaccination. In this way, the adaptive nature of the interventions enables more efficient use of vaccine resources than achieved under nonadaptive policies.