Dynamics of information-awareness-epidemic-activity coevolution in multiplex networks
Jie Chen, Mao-Bin Hu, Jinde Cao
Abstract
Epidemic spreading and awareness diffusion are typically driven by information exchange and physical contact generated by activities, respectively, evolving in a synergistic manner. In response to this reality, we propose a dynamic model of information-awareness-epidemic-activity coevolution on a four-layer network. Our findings reveal the presence of an optimal coupling between information contact preference and activity contact preference, which efficiently suppresses epidemic spreading. Specifically, the disease-related information should be targeted towards individuals who engage in more activities, enhancing their awareness and resistance to infection. Examining the epidemic situation, we observe that the epidemic threshold can be moderately increased with higher information levels but significantly decreased with increased activity frequency. Quantitatively, we establish that the epidemic threshold is strictly inversely proportional to the activity frequency. By integrating the microscopic Markov chain approach with the mean-field method, we provide theoretical insights into the system's state size and epidemic threshold. We derive an explicit expression for the critical combination of information level and activity frequency required to prevent epidemic outbreaks. These results are consistently supported by extensive Monte Carlo simulations on both heterogeneous scale-free multiplex networks and homogeneous Erd\ifmmode \mbox{\H{o}}\else \H{o}\fi{}s-R\'enyi multiplex networks. This research emphasizes the crucial importance of reducing physical contact through activities as a key preventive measure against epidemics, complementing the focus on information dissemination to raise awareness.