竞争舆论对流行病传播的影响及其应用

IF 7.9 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2025-06-06 DOI:10.1109/TNSE.2025.3577195

Qingsong Liu;Guangjie Wang

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引用次数: 0

摘要

社会对流行病的意见在政府部门控制传染病传播方面发挥了重要作用。然而，分析和了解社区意见对流行病影响的最有效方法之一是建立有效的数学模型。本文提出了一个非线性离散时间动力学模型来研究竞争舆论对传染病传播的影响。对于具有合作和竞争互动的社会网络，从基于意见的再现数得到了保证健康均衡和不健康均衡稳定的充分条件。通过引入顽固社区，揭示了流行病的消失或共存取决于社区感染的初始水平。基于对美国居民样本的真实调查数据，我们采用所提出的非线性疫情舆情模型，分别在人际接触网络、区域出行网络和芝加哥交通网络中探讨非药物干预对COVID-19的影响。进一步验证了非药物干预对减少感染有显著的积极影响。

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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The Impact of Competitive Opinion on Epidemic Spreading and Its Applications

The community's opinion on epidemics has played an important role in government departments controlling the spread of infectious diseases. However, one of the most effective ways to analyze and understand the impact of community opinions on epidemics is to establish an effective mathematical model. In this paper, we propose a nonlinear discrete-time dynamics model to investigate the impact of the competitive opinion on the epidemic spreading. For the social network with cooperative and competitive interactions, sufficient conditions guaranteeing the stability of healthy equilibrium and unhealthy equilibrium are obtained in terms of the opinion based reproduction number. By introducing the stubborn community, it is revealed that the disappearance or coexistence of the epidemic depends on the initial level of the community infection. Based on the real data from a survey conducted on a sample of U.S. residents, we employ the proposed nonlinear epidemic-opinion model to explore the impacts of the non-pharmaceutical interventions on COVID-19 in human contact network, region traveling network and Chicago transportation network, respectively. It is further validated that the non-pharmacological interventions have a significant positive impact on reducing infection.

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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.