  Many public health threats exist, motivating the need to find optimal
intervention strategies. Given the stochastic nature of the threats (e.g., the
spread of pandemic influenza, the occurrence of drug overdoses, and the
prevalence of alcohol-related threats), deterministic optimization approaches
may be inappropriate. In this paper, we implement a stochastic optimization
method to address aspects of the 2009 H1N1 and the COVID-19 pandemics, with the
spread of disease modeled by the open source Monte Carlo simulations, FluTE and
Covasim, respectively. Without testing every possible option, the objective of
the optimization is to determine the best combination of intervention
strategies so as to result in minimal economic loss to society. To reach our
objective, this application-oriented paper uses the discrete simultaneous
perturbation stochastic approximation method (DSPSA), a recursive
simulation-based optimization algorithm, to update the input parameters in the
disease simulation software so that the output iteratively approaches minimal
economic loss. Assuming that the simulation models for the spread of disease
(FluTE for H1N1 and Covasim for COVID-19 in our case) are accurate
representations for the population being studied, the simulation-based strategy
we present provides decision makers a powerful tool to mitigate potential human
and economic losses from any epidemic. The basic approach is also applicable in
other public health problems, such as opioid abuse and drunk driving.
