Nonlinear dynamics of epidemic spreading with awareness-regulated resource allocation in simplicial complexes

In this paper, we propose a coupled spreading model in simplicial complexes that integrates higher-order epidemic spreading with resource allocation driven by collective awareness. The model systematically characterizes the joint effects of awareness, resources and higher order interactions on epidemic dynamics. Theoretically, we adopt a modified heterogeneous mean-field framework to quantitatively characterize the coupled spreading dynamics, and derive an explicit expression for the epidemic threshold in terms of collective awareness and higher-order interaction strength. Our analysis reveals that the collective awareness-regulated resource allocation directly affects the recovery rate of infected individuals, which alters the disease persistence and fundamentally influences the epidemic spreading. Specifically, awareness-driven allocation substantially raises the invasion threshold and can even prevent outbreak of the epidemic, while strong higher-order interactions facilitate the spread of epidemic. By analyzing the stability of the system, we identify bistability and a saddle–node bifurcation with hysteresis loop. Simulation experiments verify the results of the theoretical analysis, and the following conclusions are further obtained: (i) the invasion threshold increases with collective awareness and diverges when awareness is sufficiently high; (ii) higher-order structures accelerate spreading and reduce both invasion and persistence thresholds, and the persistence threshold vanishes when higher-order interaction strength exceeds a critical value; and (iii) collective awareness and higher-order effects show a competitive relationship, where sufficient awareness can dominate and suppress epidemics. This study incorporates collective awareness as a regulatory factor in resource allocation, which establishes a unified framework that connects population-level perception, resource allocation and epidemic spreading. The results of this paper advance the theoretical understanding of epidemic dynamics in higher-order networks.