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多重网络上负面信息与疫情的协同演化传播动力学

Coevolving spreading dynamics of negative information and epidemic on multiplex networks.

作者信息

Chen Jiaxing, Liu Ying, Yue Jing, Duan Xi, Tang Ming

机构信息

School of Computer Science, Southwest Petroleum University, Chengdu, 610500 China.

Tianjin Key Lab of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, 300384 China.

出版信息

Nonlinear Dyn. 2022;110(4):3881-3891. doi: 10.1007/s11071-022-07776-x. Epub 2022 Aug 23.

Abstract

The widespread dissemination of negative information on vaccine may arise people's concern on the safety of vaccine and increase their hesitancy in vaccination, which can seriously impede the progress of epidemic control. Existing works on information-epidemic coupled dynamics focus on the suppression effects of information on epidemic. Here we propose a negative information and epidemic coupled propagation model on two-layer multiplex networks to study the effects of negative information of vaccination on epidemic spreading, where the negative information propagates on the virtual communication layer and the disease spreads on the physical contact layer. In our model, an individual getting an adverse event after vaccination will spread negative information and an individual affected by the negative information will reduce his/her willingness to get vaccinated and spread the negative information. By using the microscopic Markov chain method, we analytically predict the epidemic threshold and final infection density, which agree well with simulation results. We find that the spread of negative information leads to a lower epidemic outbreak threshold and a higher final infection density. However, the individuals' vaccination activities, but not the negative information spreading, has a leading impact on epidemic spreading. Only when the individuals obviously reduce their vaccination willingness due to negative information, the negative information can impact the epidemic spreading significantly.

摘要

疫苗负面信息的广泛传播可能引发人们对疫苗安全性的担忧,并增加他们对接种疫苗的犹豫,这会严重阻碍疫情防控进程。现有的信息-疫情耦合动力学研究主要关注信息对疫情的抑制作用。在此,我们提出一种基于双层多重网络的负面信息与疫情耦合传播模型,以研究疫苗负面信息对疫情传播的影响,其中负面信息在虚拟通信层传播,疾病在物理接触层传播。在我们的模型中,接种疫苗后出现不良事件的个体将传播负面信息,而受到负面信息影响的个体将降低其接种意愿并传播负面信息。通过使用微观马尔可夫链方法,我们解析预测了疫情阈值和最终感染密度,结果与模拟结果吻合良好。我们发现负面信息的传播导致较低的疫情爆发阈值和较高的最终感染密度。然而,个体的疫苗接种行为而非负面信息传播对疫情传播具有主导影响。只有当个体因负面信息而明显降低其接种意愿时,负面信息才会对疫情传播产生显著影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6870/9395805/a1d7d5b0fb8c/11071_2022_7776_Fig1_HTML.jpg

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