Guo Quantong, Jiang Xin, Lei Yanjun, Li Meng, Ma Yifang, Zheng Zhiming
School of Mathematics and Systems Science, Beihang University, Beijing 100191, China and Key Laboratory of Mathematics Informatics Behavioral Semantics (LMIB), Ministry of Education, China.
Key Laboratory of Mathematics Informatics Behavioral Semantics (LMIB), Ministry of Education, China and School of Mathematical Sciences, Peking University, Beijing 100191, China.
Phys Rev E Stat Nonlin Soft Matter Phys. 2015 Jan;91(1):012822. doi: 10.1103/PhysRevE.91.012822. Epub 2015 Jan 28.
Human awareness plays an important role in the spread of infectious diseases and the control of propagation patterns. The dynamic process with human awareness is called awareness cascade, during which individuals exhibit herd-like behavior because they are making decisions based on the actions of other individuals [Borge-Holthoefer et al., J. Complex Networks 1, 3 (2013)]. In this paper, to investigate the epidemic spreading with awareness cascade, we propose a local awareness controlled contagion spreading model on multiplex networks. By theoretical analysis using a microscopic Markov chain approach and numerical simulations, we find the emergence of an abrupt transition of epidemic threshold β(c) with the local awareness ratio α approximating 0.5, which induces two-stage effects on epidemic threshold and the final epidemic size. These findings indicate that the increase of α can accelerate the outbreak of epidemics. Furthermore, a simple 1D lattice model is investigated to illustrate the two-stage-like sharp transition at α(c)≈0.5. The results can give us a better understanding of why some epidemics cannot break out in reality and also provide a potential access to suppressing and controlling the awareness cascading systems.
人类认知在传染病传播及传播模式控制中发挥着重要作用。具有人类认知的动态过程被称为认知级联，在此过程中个体表现出从众行为，因为他们依据其他个体的行为来做决策[博尔赫 - 霍尔托费尔等人，《复杂网络杂志》1, 3 (2013)]。在本文中，为研究具有认知级联的传染病传播，我们提出了一种在多重网络上的局部认知控制传染传播模型。通过使用微观马尔可夫链方法的理论分析和数值模拟，我们发现当局部认知比率α接近0.5时，会出现疫情阈值β(c)的突然转变，这对疫情阈值和最终疫情规模产生两阶段效应。这些发现表明α的增加会加速疫情爆发。此外，研究了一个简单的一维晶格模型以说明在α(c)≈0.5时类似两阶段的急剧转变。这些结果能让我们更好地理解为何现实中有些传染病无法爆发，也为抑制和控制认知级联系统提供了一种潜在途径。
