The optimal variable stop time vaccination strategy can be found by searching the policy space (i.e. pairs of fractions-tovaccinate a and stopping thresholds c) for the policy that most frequently produces the lowest expected cost. The calculation of the optimal policy therefore explicitly accounts for uncertainty associated with the disease transmission and recovery processes (see Methods) under a given valuation of the model parameters. Assuming a value of 2 cost units per dose of vaccine (c v~2 ), we use Monte Carlo simulation to estimate the expected cost surface associated with variable stop time policies based on the true parameter values (Figure 3 ). The minimum expected cost is achieved under a policy of maximum (30%) vaccination and a stopping threshold of 150 individuals. Repeated simulation of the epidemic under this policy shows that in the average case (dashed line), the policy amounts to 4 time units of maximum vaccination as soon as the initial lag is over ( Figure 4 ). In situations where the number of susceptibles remaining after the lag is already below 150 individuals, no policy is implemented. The 95% central interval for the final distribution of total vaccine units dispensed is (339,581), representing variation in the total size of the epidemic at the time of the vaccination sweep, and the numbers of new infections after vaccination begins. Figure 5 shows the distribution of total costs accrued under this policy. After the end of the vaccination campaign, the uncertainty bands widen, representing variations in the costs associated with maintaining the remaining population of infected individuals until their natural recoveries. The mean total cost at time 40 is 1652 cost units, approximately a 21% reduction in total cost compared to no-intervention. 