Epidemic spread dynamics in multilayer networks: Probing the impact of information outbreaks and reception games.

IF 2.7 2区 数学 Q1 MATHEMATICS, APPLIED
Chaos Pub Date : 2025-03-01 DOI:10.1063/5.0236359
Jianbo Wang, Haoxing He, Ping Li, Zhanwei Du, Xiaoke Xu
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引用次数: 0

Abstract

The co-evolution of epidemic and information spread within multilayer networks is a current hot topic in network science. During epidemic outbreaks, the accompanying information exhibits both outbreak and reception game behaviors; yet, these complex phenomena have been scarcely addressed in existing research. In this paper, we model information outbreaks using activated individuals who transmit messages to their neighbors, while also considering the game behaviors of information receivers. By focusing on these two factors, we establish a multilayer network model featuring both information outbreaks and reception games. Employing the microscopic Markov chain method, we analyze the propagation dynamics within this network and derive epidemic thresholds, corroborating these results with Monte Carlo simulations. Our findings indicate that information outbreaks suppress epidemic outbreaks, whereas increased costs of information reception promote epidemic spread. Smooth information dissemination further inhibits the transmission of the epidemic. Additionally, we observe that heterogeneity in the network structure between the virtual and physical layers reduces the ultimate scale of epidemic infection, with the virtual layer exerting a more substantial influence. These insights are crucial for elucidating the co-evolutionary mechanisms of spread within multilayer networks and for developing effective epidemic prevention and control strategies.

多层网络中的流行病传播动态:探讨信息爆发和接收博弈的影响。
多层网络中疫情与信息传播的协同演化是当前网络科学研究的热点问题。在疫情爆发过程中,伴随信息同时表现出爆发和接收的博弈行为;然而,这些复杂的现象在现有的研究中几乎没有得到解决。本文在考虑信息接收者博弈行为的同时,利用激活个体向其邻居发送信息来建立信息爆发模型。围绕这两个因素,我们建立了兼具信息爆发和接收博弈的多层网络模型。采用微观马尔可夫链方法,分析了该网络内的传播动态,推导了流行阈值,并通过蒙特卡罗模拟验证了这些结果。我们的研究结果表明,信息的爆发抑制了疫情的爆发,而信息接收成本的增加则促进了疫情的传播。畅通的信息传播进一步抑制了疫情的传播。此外,我们观察到,虚拟层和物理层之间网络结构的异质性降低了流行病感染的最终规模,虚拟层的影响更大。这些见解对于阐明多层网络内传播的共同进化机制以及制定有效的流行病预防和控制战略至关重要。
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
Chaos
Chaos 物理-物理:数学物理
CiteScore
5.20
自引率
13.80%
发文量
448
审稿时长
2.3 months
期刊介绍: Chaos: An Interdisciplinary Journal of Nonlinear Science is a peer-reviewed journal devoted to increasing the understanding of nonlinear phenomena and describing the manifestations in a manner comprehensible to researchers from a broad spectrum of disciplines.
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