A stochastic optimal control strategy for multi-strain COVID-19 spread

Abstract

In this study, we propose an advanced stochastic mathematical model that delves into the intricate dynamics of multi-strain COVID-19 transmission. By accounting for environmental fluctuations, we introduce white noise into each compartment of the multi-strain system, enriching our understanding of its behavior. Rigorous proofs establish the system’s existence and uniqueness, providing a robust foundation for further exploration. We investigate key control measures within the model, specifically focusing on vaccination and targeted treatment for each strain’s compartment, as potent strategies to curtail the spread of multi-strain COVID-19. Moreover, our analysis extends to the realm of stochastic optimal control, where we examine the associated optimality conditions of the stochastic maximum Pontryagin. The ultimate goal is to reduce infections through precise control measures, paving the way for evidence-based policies that can effectively manage the pandemic’s impact. By offering deep insights into multi-strain COVID-19 propagation, our innovative model contributes significantly to the fight against the virus, guiding the development of proactive strategies and public health interventions.