Information-disease coupling dynamics with message fatigue and behavioral responses in higher-order weighted networks

In the coevolution of information-disease coupled transmission, information dissemination can effectively suppress epidemic outbreaks. However, excessive information spread can lead to message fatigue and reduces individuals’ acceptance of the information. Direct interactions between individuals (pairwise relationships) and higher-order interactions within various group environments (such as the effects of information dissemination in groups of three or more) exhibit significant differences in intensity and transmission efficiency. Additionally, individuals’ behavioral responses to epidemics often drive the coevolution of network structures and spreading systems. Aiming to address the inadequate characterization of the dynamic correlation between individual interaction heterogeneity and behavioral responses in traditional models, we propose a novel information-disease coupled model (UAF-SIS). At the information layer, we construct a weighted higher-order network based on 2-simplex structures, systematically analyzing the impacts of three weighting mechanisms – positive correlation, negative correlation, and random correlation – on the dynamics of coupled transmission. At the disease layer, we introduce an activity-driven networks with attractiveness model and develop a coevolution framework for network structures and transmission systems by integrating individuals’ behavioral responses to diseases, such as proactive isolation and social avoidance. The epidemic outbreak threshold is derived using the microscopic Markov chain method (MMCA), and theoretical results are validated through Monte Carlo (MC) simulations. Experimental findings demonstrate that the message fatigue effect underscores the necessity of controlling the dissemination frequency of social media information in public management: excessively high-frequency propagation significantly diminishes individuals’ acceptance of information. The impacts of different weighting mechanisms on the coupled transmission process vary considerably: positive correlation weighting enhances the effect of information in suppressing disease transmission, while negative correlation weighting may amplify the risks of disease prevalence. Furthermore, individuals’ behavioral responses and the time-varying characteristics of the disease layer jointly drive the evolution of network structure, significantly influencing the trajectory of transmission dynamics. Our research not only provides practical recommendations for public health management but also establishes a theoretical foundation for understanding the complex mechanisms of information-disease coupled transmission.