Liu Quan-Hui, Wang Wei, Tang Ming, Zhang Hai-Feng
Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 611731, China.
Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China.
Sci Rep. 2016 May 9;6:25617. doi: 10.1038/srep25617.
Information diffusion and disease spreading in communication-contact layered network are typically asymmetrically coupled with each other, in which disease spreading can be significantly affected by the way an individual being aware of disease responds to the disease. Many recent studies have demonstrated that human behavioral adoption is a complex and non-Markovian process, where the probability of behavior adoption is dependent on the cumulative times of information received and the social reinforcement effect of the cumulative information. In this paper, the impacts of such a non-Markovian vaccination adoption behavior on the epidemic dynamics and the control effects are explored. It is found that this complex adoption behavior in the communication layer can significantly enhance the epidemic threshold and reduce the final infection rate. By defining the social cost as the total cost of vaccination and treatment, it can be seen that there exists an optimal social reinforcement effect and optimal information transmission rate allowing the minimal social cost. Moreover, a mean-field theory is developed to verify the correctness of simulation results.
通信接触分层网络中的信息传播与疾病传播通常相互不对称耦合,其中疾病传播会受到个体对疾病认知的反应方式的显著影响。最近的许多研究表明,人类行为采纳是一个复杂的非马尔可夫过程,行为采纳的概率取决于接收到的信息的累积次数以及累积信息的社会强化效应。本文探讨了这种非马尔可夫疫苗接种采纳行为对疫情动态和控制效果的影响。研究发现,通信层中的这种复杂采纳行为可以显著提高疫情阈值并降低最终感染率。通过将社会成本定义为疫苗接种和治疗的总成本,可以看出存在一个最优社会强化效应和最优信息传输率以使社会成本最小化。此外,还发展了一种平均场理论来验证模拟结果的正确性。