  A crucial aspect of managing a public health crisis is to effectively balance
prevention and mitigation strategies, while taking their socio-economic impact
into account. In particular, determining the influence of different
non-pharmaceutical interventions (NPIs) on the effective use of public
resources is an important problem, given the uncertainties on when a vaccine
will be made available. In this paper, we propose a new approach for obtaining
optimal policy recommendations based on epidemiological models, which can
characterize the disease progression under different interventions, and a
look-ahead reward optimization strategy to choose the suitable NPI at different
stages of an epidemic. Given the time delay inherent in any epidemiological
model and the exponential nature especially of an unmanaged epidemic, we find
that such a look-ahead strategy infers non-trivial policies that adhere well to
the constraints specified. Using two different epidemiological models, namely
SEIR and EpiCast, we evaluate the proposed algorithm to determine the optimal
NPI policy, under a constraint on the number of daily new cases and the primary
reward being the absence of restrictions.
