中国物理B ›› 2025, Vol. 34 ›› Issue (11): 118701-118701.doi: 10.1088/1674-1056/ade067

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Coupled dynamics of information diffusion and disease transmission considering vaccination and time-varying forgetting probability

Lai-Jun Zhao(赵来军)1,2, Lu-Ping Chen(陈陆平)1, Ping-Le Yang(杨平乐)1,2,†, Fan-Yuan Meng(孟凡圆)3, and Chen Dong(董晨)4   

  1. 1 Business School, University of Shanghai for Science and Technology, Shanghai 200093, China;
    2 School of Intelligent Emergency Management, University of Shanghai for Science and Technology, Shanghai 200093, China;
    3 Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, China;
    4 School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang 212100, China
  • 收稿日期:2025-03-14 修回日期:2025-05-15 接受日期:2025-06-04 出版日期:2025-10-30 发布日期:2025-11-24
  • 通讯作者: Ping-Le Yang E-mail:plyang@usst.edu.cn
  • 基金资助:
    Project supported by the National Social Science Foundation of China (Grant Nos. 21BGL217 and 22CGL050) and the Philosophy and Social Science Fund of Education Department of Jiangsu Province (Grant No. 2020SJA2346).

Coupled dynamics of information diffusion and disease transmission considering vaccination and time-varying forgetting probability

Lai-Jun Zhao(赵来军)1,2, Lu-Ping Chen(陈陆平)1, Ping-Le Yang(杨平乐)1,2,†, Fan-Yuan Meng(孟凡圆)3, and Chen Dong(董晨)4   

  1. 1 Business School, University of Shanghai for Science and Technology, Shanghai 200093, China;
    2 School of Intelligent Emergency Management, University of Shanghai for Science and Technology, Shanghai 200093, China;
    3 Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, China;
    4 School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang 212100, China
  • Received:2025-03-14 Revised:2025-05-15 Accepted:2025-06-04 Online:2025-10-30 Published:2025-11-24
  • Contact: Ping-Le Yang E-mail:plyang@usst.edu.cn
  • Supported by:
    Project supported by the National Social Science Foundation of China (Grant Nos. 21BGL217 and 22CGL050) and the Philosophy and Social Science Fund of Education Department of Jiangsu Province (Grant No. 2020SJA2346).

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摘要： Vaccination is critical for controlling infectious diseases, but negative vaccination information can lead to vaccine hesitancy. To study how the interplay between information diffusion and disease transmission impacts vaccination and epidemic spread, we propose a novel two-layer multiplex network model that integrates an unaware-acceptant-negative-unaware (UANU) information diffusion model with a susceptible-vaccinated-exposed-infected-susceptible (SVEIS) epidemiological framework. This model includes individual exposure and vaccination statuses, time-varying forgetting probabilities, and information conversion thresholds. Through the microscopic Markov chain approach (MMCA), we derive dynamic transition equations and the epidemic threshold expression, validated by Monte Carlo simulations. Using MMCA equations, we predict vaccination densities and analyze parameter effects on vaccination, disease transmission, and the epidemic threshold. Our findings suggest that promoting positive information, curbing the spread of negative information, enhancing vaccine effectiveness, and promptly identifying asymptomatic carriers can significantly increase vaccination rates, reduce epidemic spread, and raise the epidemic threshold.

关键词: information diffusion, epidemic spreading, vaccine immunization, time-varying forgetting probability

Abstract: Vaccination is critical for controlling infectious diseases, but negative vaccination information can lead to vaccine hesitancy. To study how the interplay between information diffusion and disease transmission impacts vaccination and epidemic spread, we propose a novel two-layer multiplex network model that integrates an unaware-acceptant-negative-unaware (UANU) information diffusion model with a susceptible-vaccinated-exposed-infected-susceptible (SVEIS) epidemiological framework. This model includes individual exposure and vaccination statuses, time-varying forgetting probabilities, and information conversion thresholds. Through the microscopic Markov chain approach (MMCA), we derive dynamic transition equations and the epidemic threshold expression, validated by Monte Carlo simulations. Using MMCA equations, we predict vaccination densities and analyze parameter effects on vaccination, disease transmission, and the epidemic threshold. Our findings suggest that promoting positive information, curbing the spread of negative information, enhancing vaccine effectiveness, and promptly identifying asymptomatic carriers can significantly increase vaccination rates, reduce epidemic spread, and raise the epidemic threshold.

Key words: information diffusion, epidemic spreading, vaccine immunization, time-varying forgetting probability

中图分类号:  (Dynamics of evolution)

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87.23.Ge (Dynamics of social systems) 64.60.aq (Networks)