Management policies for influenza outbreaks balance the expected morbidity and mortality costs versus the cost of intervention policies. We present a methodology for dynamic determination of optimal policies in a completely observed stochastic compartmental model with parameter uncertainty. Our approach is simulation-based and searches the full set of sequential control strategies. For each time point, it generates a policy map describing the optimal intervention to implement as a function of outbreak state and Bayesian parameter posteriors. As a running example, we study a stochastic SIR model with isolation and vaccination as two possible interventions. Numerical simulations based on a classic influenza outbreak are used to explore the impact of various cost structures on management policies. Comparisons demonstrate the realized cost savings of choosing interventions based on the computed dynamic policy over simpler decision rules.