Here we address the question of how to dynamically propagate uncertainty in order to respond to an emerging epidemic while simultaneously and continuously learning about its underlying transmission dynamics. Estimation of model parameters is facilitated by regarding the transmission dynamics as stochastic processes rather than deterministic solutions to a structural equation model. This allows us to explicitly account for uncertainty in both model parameters and disease transmission. We consider a very general class of vaccination strategies defined by a fraction of the current susceptible population to be targeted for vaccination, and a threshold number of susceptibles such that once the number of susceptibles falls below this threshold, the vaccination campaign is called off. We demonstrate the calculation and application of optimal strategies of this form when coupled with iteratively updated parameter estimates using simulations based on a well-studied influenza outbreak [9] . Our emphasis is not on the realism of the underlying SIR model (though it has been shown that even simplistic transmission models can provide good fit to actual data [10] ), but rather to describe an effective approach for combining estimation and policy calculation. Permitting greater flexibility in the form of the possible intervention renders calculation of optimal intervention strategies analytically intractable, thus requiring evaluation by Monte Carlobased methods. Once in a Monte Carlo-based framework, it becomes straightforward to couple the evaluation of intervention strategies with Bayesian procedures for performing on-line estimation of parameters of the underlying epidemic model, thereby propagating parameter uncertainty through to policy decisions.