Effects of individual social skills heterogeneity and reinforcement mechanisms on co-evolution of disease and information within hypernetworks

Individual interactions serve as the fundamental mechanism for spreading phenomena that occur in networks. Traditional link networks are typically used to describe pairwise interactions, while higher-order interactions are inadequately described. Hypernetworks, by contrast, provide effective tools for modeling interactions among multiple individuals. In this paper, we examine co-evolution of disease and information within hypernetworks, considering individual social skill heterogeneity and the reinforcing effects of higher-order interactions. We solve evolving equations of individual states and disease spread thresholds using the micro-Markov chain approach. Experimental results suggest that enhanced individual social skills facilitate the spread of both disease and information. In addition, increasing individual social skills within the information layer while reducing them in the disease layer is more conducive to controlling disease transmission. Moreover, celebrities have a greater impact on the spread of disease and information than the general population. Finally, the reinforcement effect promotes the spread of disease and information compared to pairwise networks.