IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i20p13036-d939719.html
   My bibliography  Save this article

The SEIR Dynamic Evolutionary Model with Markov Chains in Hyper Networks

Author

Listed:
  • Jia Wang

    (School of Science, Dalian Maritime University, Dalian 116026, China)

  • Zhiping Wang

    (School of Science, Dalian Maritime University, Dalian 116026, China)

  • Ping Yu

    (School of Science, Dalian Maritime University, Dalian 116026, China)

  • Peiwen Wang

    (School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China)

Abstract

In real life, individuals play an important role in the social networking system. When an epidemic breaks out the individual’s recovery rate depends heavily on the social network in which he or she lives. For this reason, in this paper a nonlinear coupling dynamic model on the hyper network was built. The upper layer is the dynamic social network under the hypernetwork vision, and the lower layer is the physical contact layer. Thus, the dynamic evolutionary coupling mechanism between the social network and epidemic transmission was established. At the same time, this paper deduced the evolution process of the dynamic system according to the Markov chain method. The probability equation of the dynamic evolution process was determined, and the threshold of epidemic spread on the non-uniform network was obtained. In addition, numerical simulations verified the correctness of the theory and the validity of the model. The results show that an individual’s recovery state will be affected by the individual’s social ability and the degree of information forgetting. Finally, suitable countermeasures are suggested to suppress the pandemic from spreading in response to the coupling model’s affecting factors.

Suggested Citation

  • Jia Wang & Zhiping Wang & Ping Yu & Peiwen Wang, 2022. "The SEIR Dynamic Evolutionary Model with Markov Chains in Hyper Networks," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13036-:d:939719
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/20/13036/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/20/13036/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fan, Chong-jun & Jin, Yang & Huo, Liang-an & Liu, Chen & Yang, Yun-peng & Wang, Ya-qiong, 2016. "Effect of individual behavior on the interplay between awareness and disease spreading in multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 523-530.
    2. Shi, Tianyu & Long, Ting & Pan, Yaohui & Zhang, Wensi & Dong, Chao & Yin, Qiuju, 2019. "Effects of asymptomatic infection on the dynamical interplay between behavior and disease transmission in multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    3. Ran, Maojie & Chen, Jiancu, 2021. "An information dissemination model based on positive and negative interference in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    4. Wang, Zhishuang & Guo, Quantong & Sun, Shiwen & Xia, Chengyi, 2019. "The impact of awareness diffusion on SIR-like epidemics in multiplex networks," Applied Mathematics and Computation, Elsevier, vol. 349(C), pages 134-147.
    5. Chao Zuo & Anjing Wang & Fenping Zhu & Zeyang Meng & Xueke Zhao & xiaoke xu, 2021. "A New Coupled Awareness-Epidemic Spreading Model with Neighbor Behavior on Multiplex Networks," Complexity, Hindawi, vol. 2021, pages 1-14, March.
    6. Saif, M. Ali, 2019. "Epidemic threshold for the SIRS model on the networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    7. Koeneman, Scott H. & Cavanaugh, Joseph E., 2022. "An improved asymptotic test for the Jaccard similarity index for binary data," Statistics & Probability Letters, Elsevier, vol. 184(C).
    8. Ping Huang & Xiao-Long Chen & Ming Tang & Shi-Min Cai & Ye Wu, 2021. "Coupled Dynamic Model of Resource Diffusion and Epidemic Spreading in Time-Varying Multiplex Networks," Complexity, Hindawi, vol. 2021, pages 1-11, March.
    9. Wang, Huan & Ma, Chuang & Chen, Han-Shuang & Zhang, Hai-Feng, 2021. "Effects of asymptomatic infection and self-initiated awareness on the coupled disease-awareness dynamics in multiplex networks," Applied Mathematics and Computation, Elsevier, vol. 400(C).
    10. Huang, He & Chen, Yahong & Ma, Yefeng, 2021. "Modeling the competitive diffusions of rumor and knowledge and the impacts on epidemic spreading," Applied Mathematics and Computation, Elsevier, vol. 388(C).
    11. Jia, Mengqi & Li, Xin & Ding, Li, 2021. "Epidemic spreading with awareness on multi-layer activity-driven networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 579(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Benyu & Gu, Yijun & Zheng, Diwen, 2022. "Community detection in error-prone environments based on particle cooperation and competition with distance dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    2. Yu, Jiating & Leng, Jiacheng & Sun, Duanchen & Wu, Ling-Yun, 2023. "Network Refinement: Denoising complex networks for better community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 617(C).
    3. Lei, Mingli, 2022. "Information dimension based on Deng entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    4. Li, Rui & Xu, Bang-Lin & Chen, De-Bao & Zhou, Jian-Fang & Yuan, Wu-Jie, 2023. "Transitions to synchronization induced by synaptic increasing in coupled tonic neurons with electrical synapses," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ma, Weicai & Zhang, Peng & Zhao, Xin & Xue, Leyang, 2022. "The coupled dynamics of information dissemination and SEIR-based epidemic spreading in multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    2. Huang, He & Chen, Yahong & Yan, Zhijun, 2021. "Impacts of social distancing on the spread of infectious diseases with asymptomatic infection: A mathematical model," Applied Mathematics and Computation, Elsevier, vol. 398(C).
    3. Huo, Liang’an & Gu, Jiafeng, 2023. "The influence of individual emotions on the coupled model of unconfirmed information propagation and epidemic spreading in multilayer networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    4. Zhu, Linhe & Liu, Wenshan & Zhang, Zhengdi, 2021. "Interplay between epidemic and information spreading on multiplex networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 188(C), pages 268-279.
    5. Juang, Jonq & Liang, Yu-Hao, 2024. "Epidemic models in well-mixed multiplex networks with distributed time delay," Applied Mathematics and Computation, Elsevier, vol. 474(C).
    6. Wu, Bingjie & Huo, Liang'an, 2024. "The influence of different government policies on the co-evolution of information dissemination, vaccination behavior and disease transmission in multilayer networks," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    7. Hu, Xin & Wang, Zhishuang & Sun, Qingyi & Chen, Jiaxing & Zhao, Dawei & Xia, Chengyi, 2024. "Coupled propagation between one communicable disease and related two types of information on multiplex networks with simplicial complexes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 645(C).
    8. Zhang, Yuexia & Pan, Dawei, 2021. "Layered SIRS model of information spread in complex networks," Applied Mathematics and Computation, Elsevier, vol. 411(C).
    9. Wang, Runzhou & Zhang, Xinsheng & Wang, Minghu, 2024. "A two-layer model with partial mapping: Unveiling the interplay between information dissemination and disease diffusion," Applied Mathematics and Computation, Elsevier, vol. 468(C).
    10. Li, Dandan & Xie, Weijie & Han, Dun, 2024. "Multi-information and epidemic coupling propagation considering indirect contact on two-layer networks," Applied Mathematics and Computation, Elsevier, vol. 474(C).
    11. Huang, He & Pan, Jialin & Chen, Yahong, 2024. "The competitive diffusion of knowledge and rumor in a multiplex network: A mathematical model," Applied Mathematics and Computation, Elsevier, vol. 475(C).
    12. Wang, Jun & Cai, Shimin & Wang, Wei & Zhou, Tao, 2023. "Link cooperation effect of cooperative epidemics on complex networks," Applied Mathematics and Computation, Elsevier, vol. 437(C).
    13. Ping Yu & Peiwen Wang & Zhiping Wang & Jia Wang, 2022. "Supply Chain Risk Diffusion Model Considering Multi-Factor Influences under Hypernetwork Vision," Sustainability, MDPI, vol. 14(14), pages 1-15, July.
    14. Huo, Liang’an & Chen, Sijing, 2020. "Rumor propagation model with consideration of scientific knowledge level and social reinforcement in heterogeneous network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    15. Huang, He & Chen, Yahong & Ma, Yefeng, 2021. "Modeling the competitive diffusions of rumor and knowledge and the impacts on epidemic spreading," Applied Mathematics and Computation, Elsevier, vol. 388(C).
    16. Wang, Haiying & Moore, Jack Murdoch & Wang, Jun & Small, Michael, 2021. "The distinct roles of initial transmission and retransmission in the persistence of knowledge in complex networks," Applied Mathematics and Computation, Elsevier, vol. 392(C).
    17. Jia He & Miao Ma & Yuxuan Zhou & Miaoke Wang, 2023. "What We Have Learned about the Characteristics and Differences of Disaster Information Behavior in Social Media—A Case Study of the 7.20 Henan Heavy Rain Flood Disaster," Sustainability, MDPI, vol. 15(6), pages 1-18, March.
    18. An, Xuming & Ding, Li & Hu, Ping, 2020. "Information propagation with individual attention-decay effect on activity-driven networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    19. Wu, Jiang & Zuo, Renxian & He, Chaocheng & Xiong, Hang & Zhao, Kang & Hu, Zhongyi, 2022. "The effect of information literacy heterogeneity on epidemic spreading in information and epidemic coupled multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    20. Wang, Mengyao & Pan, Qiuhui & He, Mingfeng, 2020. "The effect of individual attitude on cooperation in social dilemma," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 555(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13036-:d:939719. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.