A key challenge to calculating optimal intervention strategies involves devising ways to characterize and explore the space of intervention policies. Most existing work on optimal intervention has required various limiting assumptions about the forms of such strategies. Ball and Lyne [6] considered optimal vaccination in terms of the allocation of vaccine doses to households of various sizes in an explicitly structured population model. Patel et al [7] considered optimal vaccination in terms of the allocation of vaccine doses to different age classes in an explicitly age-and geographically-structured population model. Tildesley et al [1] describe optimal vaccination strategies for a foot-and-mouth epidemic in which the optimized parameter is the size of the radius surrounding a point of infection within which all livestock are to be vaccinated. These methods are primarily concerned with pre-emptive interventions that can be completed before the arrival of the pathogen. Under such scenarios, there is no need to consider adaptive or sequentially updated interventions because as soon as the intervention policy is triggered, the threat of epidemic is eradicated. In real scenarios, such widespread vaccination may not be achievable. Moreover, these methods traditionally involve calculations that assume no uncertainty in key model parameters such as transmission rate, recovery rate, and mortality rate. Recently Elderd et al [8] , using Bayesian methods, demonstrated the importance of explicitly quantifying such underlying uncertainty when forecasting the expected efficacy of trace versus mass vaccination policies. Their findings demonstrate that accurate propagation of parameter uncertainty can sometimes reveal deep and troubling consequences of a proposed vaccination strategy, and they suggest that incorporation of such uncertainty could impact policy decisions.