Modeling coupled epidemic and awareness spreading with heterogeneous recovery and simplicial complexes

The coupled dynamics of epidemic spreading and information diffusion on multiplex networks have attracted increasing attention, yet many studies overlook social reinforcement effects and assume a fixed probability for individuals to revert from awareness to unawareness. In reality, this probability is heterogeneous: people in high-risk surroundings tend to stay alert longer, whereas those in safer environments lose vigilance more quickly. Such behavior is influenced by both local epidemic conditions (e.g., neighbors’ infection status) and global epidemic trends. Since social reinforcement often arises from group interactions beyond simple pairwise contacts, simplicial complexes provide a natural framework to capture such higher-order effects. Motivated by these limitations, we develop a heterogeneous alertness recovery mechanism informed by both local and global epidemic information, while simultaneously integrating simplicial complexes into awareness diffusion models to capture higher-order social reinforcement. Based on the microscopic Markov chain approach (MMCA) and Monte Carlo simulations, we show that this adaptive, higher-order framework enhances self-protection awareness, promotes precautionary behavior, suppresses epidemic outbreaks, and raises the epidemic threshold.