Analyzing social influence and vaccine adoption through imitation and aspiration dynamics on complex networks

Controlling infectious disease outbreaks remains a significant challenge, intensified by the evolving interplay between human behavior and epidemic dynamics. Existing models often overlook the adaptive nature of vaccination decisions, which are shaped by perceived risk and social interactions, particularly within heterogeneous contact networks. To bridge this gap, we propose an SVISIVR (Susceptible-Vaccinated-Infectious-Recovered) model integrating evolutionary game theory with epidemic dynamics on Barabási-Albert (BA) scale-free and Erdős-Rényi (ER) random networks. Individuals update their vaccination strategies by imitating neighbors and through aspiration-based learning, influenced by both local (neighborhood infection) and global (media/public health) perceptions. The model incorporates bounded rationality and delayed decision-making, capturing realistic behavioral processes. Numerical simulations examine the effects of vaccine cost, aspiration level, and disease prevalence on vaccination coverage and epidemic outcomes. Findings indicate that diverse contact networks significantly improve vaccination rates, especially when people use aspiration-based adaptation in low-cost scenarios. In addition, the interplay of imitation and aspiration dynamics enhances epidemic control, leading to a vaccine equilibrium arising from intricate spatiotemporal interactions on strategic learning. This paradigm integrates epidemiological modeling with robust human behavioral dynamics, providing insights to enhance vaccination strategies across diverse populations. It establishes the foundation for advancing epidemic game models, including multi-strategy evolution, co-evolving networks, and information-adaptive interventions.