Epidemic dynamics driven by adaptive rewiring mechanism on higher-order networks

This study investigates the dynamics of the susceptible–infected–susceptible (SIS) model on adaptive simplicial complex networks, incorporating higher-order interactions to better capture group-level contagion. By introducing an adaptive rewiring mechanism, susceptible nodes can sever links with infected neighbors and then reconnect to other susceptible nodes, dynamically reshaping the network topology, so as to effectively mitigate epidemic spreading. A pairwise-approximation approach is taken to derive analytical expressions for infection density, accounting for the interplay between adaptive rewiring and higher-order interactions. Numerical simulations verify the theoretical predictions, revealing that adaptive rewiring significantly reduces the infection density and meanwhile lifts up the epidemic threshold. Theoretical and experimental results reveal that higher-order interactions amplify bistable dynamics, leading to abrupt transitions between disease-free and endemic states. Adaptive rewiring mitigates pairwise transmission but exhibits limited efficacy in suppressing contagion driven by strong group interactions. These findings highlight the critical role of higher-order structures and network rewiring adaptation in reshaping epidemic outcomes. The proposed framework offers new insights into the design of control strategies for epidemic annihilation, emphasizing the importance of integrating higher-order interactions and network rewiring adaptation in epidemic modeling.