Epidemic evolution model considering individual heterogeneity in multi-layer hypernetwork

In recent years, the dynamic coupling mechanisms between information dissemination and epidemic transmission have garnered significant attention. Existing studies predominantly focus on the impact of individual awareness on disease spread; however, in reality, the factors driving awareness shifts and heterogeneous perceptions of epidemics vary substantially among individuals with different health statuses. Moreover, traditional pairwise interaction networks fail to capture the complexity of social contagion processes. To address these gaps, this study proposes a three-layer hypernetwork epidemic model (mass media layer–information layer–epidemic layer) based on evolutionary hypergraphs, incorporating individual heterogeneity and higher-order group interactions. The information layer employs an asthenic awareness–powerful awareness–asthenic awareness (APA) propagation model to characterize the diffusion of epidemic awareness, integrated with a perceived pain level metric to quantify dynamic awareness states among infected individuals. The underlying susceptible–infected–recovered (SIR) model incorporates dual modulation factors that adjust infection and transmission probabilities based on awareness-dependent behaviors. Model validity is verified through microscopic Markov chain approach (MMCA) numerical simulations, which identify epidemic thresholds and analyze key parameters. The key findings reveal that susceptibility and transmission rates are critical factors determining epidemic scale; high-coverage official media can rapidly disseminate accurate information and curb rumors; controlling pain levels and improving recovery efficiency are crucial for reducing the infection peak and shortening epidemic duration. This study provides a systematic analytical framework for understanding the interaction mechanisms among mass media, individual cognition, and epidemic transmission in real-world scenarios.