中国安全科学学报 ›› 2026, Vol. 36 ›› Issue (1): 257-266.doi: 10.16265/j.cnki.issn1003-3033.2026.01.0664

• 职业健康 • 上一篇    下一篇

基于多层网络的传染病与负面情绪耦合传播模型

王甜甜1(), 马海韵1, 王志荣1, 刘铁忠2,**(), 闫晓涵3   

  1. 1 南京工业大学 应急管理学院,江苏 南京 211816
    2 北京理工大学 管理学院,北京 100081
    3 河北经贸大学 金融学院,河北 石家庄 050061
  • 收稿日期:2025-08-30 修回日期:2025-11-01 出版日期:2026-01-28
  • 通信作者:
    ** 刘铁忠(1974—),男,黑龙江海伦人,博士,教授,主要从事风险分析与危机管理方面的研究。E-mail:
  • 作者简介:

    王甜甜 (1994—),女,山东德州人,博士,讲师,主要从事风险分析与应急管理等方面的研究。E-mail:

    马海韵, 教授。

    王志荣, 教授。

    闫晓涵, 讲师。

  • 基金资助:
    国家社科基金(21BGL299); 江苏省哲学社会科学联合会项目(23SSL133); 江苏高校哲学社会科学研究一般项目(2025SJYB0169); 教育部人文社会科学青年基金项目(25YCZH260)

Coupled transmission model of spatial infectious diseases and negative emotions based on multilayer networks

WANG Tiantian1(), MA Haiyun1, WANG Zhirong1, LIU Tiezhong2,**(), YAN Xiaohan3   

  1. 1 School of Emergency Management, Nanjing Tech University, Nanjing Jiangsu 211816, China
    2 School of Management, Beijing University of Technology, Beijing 100081, China
    3 School of Finance, Hebei University of Economics and Business, Shijiazhuang Hebei 050061, China
  • Received:2025-08-30 Revised:2025-11-01 Published:2026-01-28

摘要:

为揭示疾病传播强度与情绪扩散速率的协同演化关系,基于双层复杂网络结构,将经典易感-潜伏-感染-免疫(SEIR)模型与情绪传播的5状态动力学框架(Se-Ee-Ie-Ge-Re)相融合,构建耦合动力学模型(SEIR-SeEeIeGeRe),并采用微观马尔可夫链(MMC)与蒙特卡罗(MC)法仿真进行理论推演与仿真验证。研究结果表明:降低传染病感染概率虽可降低情绪传播规模,但对情绪传播高峰的抑制作用有限;而缩短传染病潜伏期与提高治愈率,可显著调控负面情绪传播,呈现情绪代谢迟滞效应与情绪引导者密度阈值效应的双阶段传播特征。此外,提高个体防疫配合度可通过阻断传播链、强化社会规范与矫正认知偏差3条路径实现,有效延缓负面情绪扩散并缩小其规模。

关键词: 多层网络, 负面情绪, 传染病, 耦合传播模型, 微观马尔可夫链(MMC)

Abstract:

To reveal the co-evolutionary relationship between disease transmission intensity and emotional diffusion rate, based on a two-layer complex network structure, this study integrated the classical Susceptible-Exposed-Infected-Recovered (SEIR) epidemic model with a five-state emotional dynamics framework (Se-Ee-Ie-Ge-Re) to construct a coupled dynamics model (SEIR-SeEeIeGeRe). Theoretical derivation and simulation validation were conducted using the microscopic Markov chain approach and Monte Carlo simulation. The findings reveal that although reducing the disease infection probability can mitigate the scale of emotional propagation, its inhibitory effect on the peak of emotional propagation remains limited. In contrast, shortening the disease incubation period and improving the recovery rate significantly regulate the spread of negative emotions, exhibiting a two-stage propagation pattern characterized by emotional metabolic hysteresis and the density threshold effect of emotional guides. Furthermore, enhancing individuals' compliance with epidemic preventive measures can effectively delay the spread and reduce the scale of negative emotions through three pathways: blocking transmission chains, reinforcing social norms, and correcting cognitive biases.

Key words: multilayer networks, negative emotion, infectious disease, coupling spreading model, Micro-Markov chain (MMC)

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