Targeted Vaccination Policy Embedded With Behavioral Dynamics and a Heterogeneous Network Setting for Controlling Epidemic Diseases

Vaccination techniques inside networks involve a critical trade-off among effectiveness, cost, and time. The proposed research work extends a quenched mean-field (QMF) susceptible–vaccinated–infected (susceptible and vaccinated)–recovered (SVISIVR) model incorporating behavioral dynamics to comprehensively evaluate static (initially vaccinating hubs) and dynamic targeting (vaccinating hubs after prevalence exceeds a threshold) mechanisms, where remaining individuals follow a voluntary vaccination policy. We investigate various scenarios, including diverse targeting fractions (0%–15%), trigger levels (1%), network architectures (scale-free, small-world, and random), vaccination efficiency, and behavioral characteristics that influence voluntary uptake. Our findings suggest that in scale-free networks, even a small hub targeting substantially increases the threshold and decreases the epidemic magnitude. Static targeting yields the most effective epidemic suppression but incurs higher initial costs, while dynamic targeting produces similar effects when activated correctly, thereby maximizing benefits relative to costs. Therefore, the cost structures, vaccine efficacy, and rates of behavioral imitation determine the optimal equilibrium between voluntary and targeted vaccination. These results connect theoretical epidemic thresholds with practical measures of cost-effectiveness, offering policymakers actionable insights for setting vaccination strategies under resource constraints.