Optimal feedback policies in stochastic epidemic models

Abstract

We consider the problem of finding optimal policies that mitigate the effects of an epidemic. We develop computational tools for finding such policies for broad classes of stochastic epidemic models and investigate various features of such policies. In particular, we observe that optimal policies are predominantly constant for epidemics where the mitigation measures are associated with the infected population.