An epidemic can be effectively summarized by the disease state of the population (i.e. the current numbers of susceptible and infected individuals) and by the SIR model parameters that define the dynamics of transmission, death, and recovery. In adaptive management, the former is used to perform inference on the latter. Each time new data are collected, Markov chain Monte Carlo (MCMC) is used to sample from the current posterior distribution on model parameters. The optimal variable stop time strategy associated with each set of sampled parameter values is calculated, and the policy that most frequently minimizes the total expected cost (over all sampled parameter values) is enacted at the next time step. The fundamental difference between the adaptive policies calculated here and those calculated in the previous section is that here, the vaccination policy is a dynamic function of the current disease state and the current distribution of each parameter, whereas before, the policy was a static function of the initial disease state and the initial point estimate of each parameter.