We have demonstrated a novel adaptive management strategy based on a relatively simple characterization of the underlying SIR model and the epidemiological cost function. In principle, this methodological framework can readily accommodate more complicated disease dynamics such as immigration, latent infected states, missing data, and vector-communicated diseases, as well as more complicated intervention strategies that allow combined vaccination and quarantine. However, the incorporation of such features is likely to impose a heavy computational burden, and so model complexity should only be increased when additional parameters are supported (and identified) by the data and demanded by the biology. As in all Bayesian analyses, care must be taken when choosing prior distributions. In this study, our primary interests required the use of vague/noninformative prior distributions, in order to demonstrate the estimability of model parameters. In practice, informative, even pessimistic priors (i.e., overestimated infectiousness and mortality, underestimated recovery) may provide useful reference points for the adaptive policy calculations, especially in situations of acute infections for which the duration of the epidemic may be too short for incoming data to dominate the prior information. In such situations, the adaptive approach still provides the opportunity for data to inform parameter values if it becomes available, while basing interventions on current parameter estimates as determined by their prior distributions.