On a Discrete-Time Networked SIV Epidemic Model With Polar Opinion Dynamics

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2024-09-19 DOI:10.1109/TNSE.2024.3464852

Qiulin Xu;Hideaki Ishii

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Abstract

This paper studies novel epidemic spreading problems influenced by opinion evolution in social networks, where the opinions reflect the public health concerns. A coupled bilayer network is proposed, where the epidemics spread over several communities through a physical network layer while the opinions evolve over the same communities through a social network layer. The epidemic spreading process is described by a susceptible-infected-vigilant (SIV) model, which introduces opinion-dependent epidemic vigilance state compared with the classical epidemic models. The opinion process is modeled by a polar opinion dynamics model, which includes infection prevalence and human stubbornness into the opinion evolution. By introducing an opinion-dependent reproduction number, we analyze the stability of disease-free and endemic equilibria and derive sufficient conditions for their global asymptotic stability. We also discuss the mutual effects between epidemic eradication and opinion consensus, and the possibility of suppressing the epidemic spreading by intervening in the opinions or implementing public health strategies. Simulations are conducted to verify the theoretical results and demonstrate the feasibility of epidemic suppression.

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关于具有极地舆论动态的离散时间网络 SIV 流行病模型

本文研究受社交网络中舆论演变影响的新型流行病传播问题，其中舆论反映了公共卫生问题。本文提出了一种耦合双层网络，流行病通过物理网络层在多个社区传播，而舆论则通过社交网络层在同一社区演化。流行病传播过程由易感-感染-警惕（SIV）模型描述，与经典流行病模型相比，该模型引入了与舆论相关的流行病警惕状态。舆论过程由极地舆论动态模型建模，该模型将感染流行率和人类的固执纳入舆论演化过程。通过引入与舆论相关的繁殖数量，我们分析了无病均衡和流行均衡的稳定性，并推导出它们全局渐近稳定性的充分条件。我们还讨论了根除疫情与舆论共识之间的相互影响，以及通过干预舆论或实施公共卫生策略来抑制疫情蔓延的可能性。我们通过模拟验证了理论结果，并证明了抑制疫情的可行性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.