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

Area of Science:

• Epidemiology
• Network Science
• Information Diffusion

Background:

• Co-occurring epidemics pose complex challenges for public health management.
• Understanding the interplay between information diffusion and disease transmission is crucial for effective intervention.

Purpose of the Study:

• To investigate the impact of multi-information diffusion on the transmission of interacting diseases.
• To analyze these dynamics under different disease interaction modes (inhibition, facilitation, asymmetry) within higher-order networks.

Main Methods:

• Formulation of a two-layer Unaware-Aware-Unaware-Susceptible-Infected-Susceptible (UAU-SIS) model.
• Representation of higher-order interactions using simplicial complexes.
• Extension of the microscopic Markov chain approach and validation via Monte Carlo simulations.

Main Results:

• Disease interaction modes significantly alter state probabilities compared to independent spreading.
• Bistability in disease dynamics persists despite multi-information interference, underscoring higher-order network effects.
• Multi-information interactions exhibit mode-specific patterns, with transmission rate changes having differential impacts based on disease interaction modes.
• Multi-information influences both the duration of disease coexistence and infection prevalence, with divergent effects across interaction modes.

Conclusions:

• Multi-information diffusion plays a critical role in modulating the dynamics of interacting epidemics.
• Insights gained can inform the development of targeted intervention strategies for complex epidemic scenarios.