Optimal residence time control for stochastically perturbed prescription opioid epidemic models

TL;DR

This paper develops an optimal control framework for a stochastic opioid epidemic model, aiming to minimize the exit rate from a safe domain by strategically controlling intervention rates and accounting for random perturbations.

Contribution

It introduces a novel stochastic control approach to optimize intervention strategies in opioid epidemic models with noise influence.

Findings

Derived an optimal Markov control for the stochastic epidemic model.

Established conditions for the existence of an optimal control strategy.

Provided insights into how stochastic perturbations affect intervention effectiveness.

Abstract

In this paper, we consider an optimal control problem for a prescription opioid epidemic model that describes the interaction between the regular prescription or addictive use of opioid drugs, and the process of rehabilitation and that of relapsing into opioid drug use. In particular, our interest is in the situation, where the control appearing linearly in the opioid epidemics is interpreted as the rate at which the susceptible individuals are effectively removed from the population due to an opioid-related intervention policy or when the dynamics of the addicted is strategically influenced due to an accessible addiction treatment facility, while a small perturbing noise enters through the dynamics of the susceptible group in the population compartmental model. To this end, we introduce a mathematical apparatus that minimizes the asymptotic exit-rate with which the solution for such stochastically perturbed prescription opioid epidemics exits from a given bounded open domain. Moreover, under certain assumptions, we also provide an admissible optimal Markov control for the corresponding optimal control problem that optimally effected removal of the susceptible or recovered individuals from the population dynamics.