The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Mathematical Science, Anhui University, Hefei 230601, China.
School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University (NWPU), Xi'an 710072, Shaanxi, China.
Chaos. 2022 Aug;32(8):083110. doi: 10.1063/5.0099183.
There has been growing interest in exploring the dynamical interplay of epidemic spreading and awareness diffusion within the multiplex network framework. Recent studies have demonstrated that pairwise interactions are not enough to characterize social contagion processes, but the complex mechanisms of influence and reinforcement should be considered. Meanwhile, the physical social interaction of individuals is not static but time-varying. Therefore, we propose a novel sUAU-tSIS model to characterize the interplay of simplicial awareness contagion and epidemic spreading on time-varying multiplex networks, in which one layer with 2-simplicial complexes is considered the virtual information layer to address the complex contagion mechanisms in awareness diffusion and the other layer with time-varying and memory effects is treated as the physical contact layer to mimic the temporal interaction pattern among population. The microscopic Markov chain approach based theoretical analysis is developed, and the epidemic threshold is also derived. The experimental results show that our theoretical method is in good agreement with the Monte Carlo simulations. Specifically, we find that the synergistic reinforcement mechanism coming from the group interactions promotes the diffusion of awareness, leading to the suppression of the spreading of epidemics. Furthermore, our results illustrate that the contact capacity of individuals, activity heterogeneity, and memory strength also play important roles in the two dynamics; interestingly, a crossover phenomenon can be observed when investigating the effects of activity heterogeneity and memory strength.
人们对于在多重网络框架内探索传染病传播和意识扩散的动态相互作用越来越感兴趣。最近的研究表明，两两相互作用不足以描述社交传染过程，而应考虑影响和增强的复杂机制。同时，个体的物理社交互动不是静态的，而是随时间变化的。因此，我们提出了一种新颖的 sUAU-tSIS 模型来描述时变多重网络上单纯形意识传播和传染病传播的相互作用，其中一层带有 2-单纯形复合物被视为虚拟信息层，以解决意识扩散中复杂的传染机制，另一层带有时变和记忆效应的则被视为物理接触层，以模拟人口之间的时间交互模式。我们还开发了基于微观马尔可夫链方法的理论分析，并推导出了传染病阈值。实验结果表明，我们的理论方法与蒙特卡罗模拟吻合得很好。具体来说，我们发现来自群体相互作用的协同增强机制促进了意识的扩散，从而抑制了传染病的传播。此外，我们的结果表明，个体的接触能力、活动异质性和记忆强度在这两个动力学中也起着重要作用；有趣的是，在研究活动异质性和记忆强度的影响时，可以观察到交叉现象。
