Modeling the co-evolution of multi-information and interacting diseases with higher-order effects

To enhance epidemic management for co-occurring diseases, we investigate how multi-information diffusion impacts the transmission of interacting diseases under three interaction modes (inhibition, facilitation, asymmetry) in higher-order networks. We formulate a two-layer Unaware-Aware-Unaware-Susceptible-Infected-Susceptible model, comprising an upper information-diffusion layer and a lower disease-transmission layer with higher-order interactions represented by simplicial complexes. Extending the microscopic Markov chain approach, we derive the evolutionary equations and validate them via Monte Carlo simulations. Key findings are as follows: (1) Disease interaction modes alter state probabilities distinctively compared to independent spreading; (2) Bistability persists despite multi-information interference, highlighting higher-order network effects; (3) Multi-information interactions show mode-specific patterns—increasing one information’s transmission rate differently affects another depending on disease interaction modes; (4) Multi-information modulates both the duration of disease coexistence and the infection prevalence; moreover, elevating the transmission rate of one information type yields divergent impacts on the prevalence of the other disease across different interaction modes. These insights advance targeted intervention strategies for interacting epidemics.