Investigating the effect of adaptive optimal control function in epidemic dynamics: predictions and strategy evolution based on SIR/V game theoretic framework
Nuruzzaman Rahat, Abid Hossain, and Muntasir Alam

TL;DR
This paper develops an adaptive optimal control framework for SIR/V epidemic models incorporating human behavioral effects and game theory, leading to more effective strategies that reduce costs and epidemic severity.
Contribution
It introduces a novel adaptive control approach within a game theoretic SIR/V model, accounting for behavioral dynamics and resource constraints, validated through numerical simulations.
Findings
Adaptive policies reduce epidemic costs compared to static strategies.
Strategies achieve lower peak infections and faster epidemic extinction.
Behavioral thresholds identified inform timely policy adjustments.
Abstract
In this paper, we consider an adaptive optimal control problem for an SIR/V epidemic model with human behavioral effects.We develop a model where effective management of infectious diseases are monitored by the means of non pharmaceutical interventions.This study develops an adaptive optimal control function within an SIR/V framework embedding a non cooperative game theoretic mechanism to capture the dynamic interplay between individual vaccination behavior and population level transmission. We derive analytical expression for the optimal control trajectory under resource constrain and heterogeneous susceptibility and we validate our model using numerical simulations,calibrated with the real world epidemic parameters. We find that for the adaptive optimal policy for a generally known SIR/V model depending on the game theoretic epidemic state leads to substantial reduction in expenses…
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Taxonomy
TopicsCOVID-19 epidemiological studies · Mathematical and Theoretical Epidemiology and Ecology Models · Game Theory and Applications
