Dynamic behavior driven resource allocation and its impact on epidemic spreading in complex networks

Epidemic spreading in complex networks is influenced not only by the network topology but also by individual behavior and resource allocation mechanisms. Traditional models often assume static or homogeneous resource allocation, neglecting the dynamic and heterogeneous adjustments of individual behavior in response to local infection states. This study introduces a dynamic behavior-driven resource allocation model, which couples the classic SIS model with sensitivity parameter and resource awareness parameter. The model captures the interaction between individual behavior, resource allocation, and epidemic dynamics through a two-layer network framework. Through extensive numerical simulations, we obtain the following findings: First, increasing the individual sensitivity can significantly raise the epidemic threshold, which effectively delaying the epidemic outbreaks. High sensitivity enables individuals to rapidly adjust their behavior, leading the reduction in contact rates and resource allocation, thereby suppressing infection spread. Second, the resource awareness of individuals demonstrates a non-monotonic influence on epidemic spreading, and an optimal range of awareness level is obtained, which can maximize epidemic suppression by balancing resource allocation and protection. Lastly, the coupling of dynamic behavior and resource allocation mechanisms provides critical insights into how local interactions can mitigate global epidemic spreading. These findings highlight the importance of incorporating behavioral and resource factors into epidemic models, offering a theoretical basis for developing adaptive public health strategies in complex network environments.