IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v181y2024ics0960077924001371.html
   My bibliography  Save this article

Co-evolution dynamics between information and epidemic with asymmetric activity levels and community structure in time-varying multiplex networks

Author

Listed:
  • Xie, Xiaoxiao
  • Huo, Liang'an

Abstract

During an epidemic outbreak, apart from individual behavior and internal factors, certain external elements typically exert a significant influence on the propagation process. This paper introduces an innovative two-layer model, denoted “unaware–aware–unaware–susceptible–infected–susceptible” (UAU-SIS) model, encompassing two dynamic processes in time-varying multiplex networks. The model considers the effects of asymmetric individual activity levels and distinct network topologies on the dynamic propagation process. Importantly, we also explore how global and local environmental stresses impact individual behavior changes. To quantify the influence of individual behavior changes on dynamic propagation, we employ the Heaviside step function. We also employ a power-law distribution to describe varying individual activity levels for each layer to characterize the influence of asymmetric individual activity levels on epidemic transmission. Using the microscopic Markov chain approach (MMCA), we establish the probabilistic transfer equation for each state and derive the epidemic outbreak threshold. Comprehensive Monte Carlo (MC) simulations were performed to validate our theoretical findings, with the results demonstrating that reducing community mobility and decreasing the threshold for behavior change can effectively delay outbreaks and limit the scale of an epidemic. The sensitivity of local environmental stresses is more capable of influencing individual behavioral decision-making. Furthermore, activity levels in the physical contact network exert a greater impact on epidemic transmission compared to those in the information diffusion layer. Notably, varying individual thresholds for behavior change result in a two-phase effect on both the epidemic outbreak threshold and its ultimate scale. Hence, this study provides actionable insights for policymakers responsible for shaping vaccination strategies and managing epidemic transmission.

Suggested Citation

  • Xie, Xiaoxiao & Huo, Liang'an, 2024. "Co-evolution dynamics between information and epidemic with asymmetric activity levels and community structure in time-varying multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    • Handle: RePEc:eee:chsofr:v:181:y:2024:i:c:s0960077924001371
    DOI: 10.1016/j.chaos.2024.114586
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077924001371
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2024.114586?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Heffner, Kathi L. & Waring, Molly E. & Roberts, Mary B. & Eaton, Charles B. & Gramling, Robert, 2011. "Social isolation, C-reactive protein, and coronary heart disease mortality among community-dwelling adults," Social Science & Medicine, Elsevier, vol. 72(9), pages 1482-1488, May.
    2. Huo, Liang’an & Yu, Yue, 2023. "The impact of the self-recognition ability and physical quality on coupled negative information-behavior-epidemic dynamics in multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    3. 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).
    4. Zou, Rongcheng & Duan, Xiaofang & Han, Zhen & Lu, Yikang & Ma, Kewei, 2023. "What information sources can prevent the epidemic: Local information or kin information?," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    5. Liu, Chen & Zhou, Li-xin & Fan, Chong-jun & Huo, Liang-an & Tian, Zhan-wei, 2015. "Activity of nodes reshapes the critical threshold of spreading dynamics in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 269-278.
    6. Gabrick, Enrique C. & Sayari, Elaheh & Protachevicz, Paulo R. & Szezech, José D. & Iarosz, Kelly C. & de Souza, Silvio L.T. & Almeida, Alexandre C.L. & Viana, Ricardo L. & Caldas, Iberê L. & Batista, , 2023. "Unpredictability in seasonal infectious diseases spread," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    7. Gao, Bo & Deng, Zhenghong & Zhao, Dawei, 2016. "Competing spreading processes and immunization in multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 93(C), pages 175-181.
    8. Marcel Salathé & James H Jones, 2010. "Dynamics and Control of Diseases in Networks with Community Structure," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-11, April.
    9. Hu, Ping & Geng, Dongqing & Lin, Tao & Ding, Li, 2021. "Coupled propagation dynamics on multiplex activity-driven networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
    10. Kabir, K.M. Ariful & Kuga, Kazuki & Tanimoto, Jun, 2019. "Analysis of SIR epidemic model with information spreading of awareness," Chaos, Solitons & Fractals, Elsevier, vol. 119(C), pages 118-125.
    Full references (including those not matched with items on IDEAS)

    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. Zhu, Hongmiao & Jin, Zhen & Yan, Xin, 2023. "A dynamics model of coupling transmission for multiple different knowledge in multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 629(C).
    2. 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).
    3. Zhou, Li-xin & Lin, Jie & Wang, Yu-qing & Li, Yan-feng & Miao, Run-sheng, 2018. "Critical phenomena of spreading dynamics on complex networks with diverse activity of nodes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 439-447.
    4. 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).
    5. Kabir, K.M. Ariful & Tanimoto, Jun, 2019. "Dynamical behaviors for vaccination can suppress infectious disease – A game theoretical approach," Chaos, Solitons & Fractals, Elsevier, vol. 123(C), pages 229-239.
    6. Gregory, Steve, 2012. "Ordered community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2752-2763.
    7. Wei Zhong, 2017. "Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 475-495, December.
    8. Chen, Dandan & Zheng, Muhua & Zhao, Ming & Zhang, Yu, 2018. "A dynamic vaccination strategy to suppress the recurrent epidemic outbreaks," Chaos, Solitons & Fractals, Elsevier, vol. 113(C), pages 108-114.
    9. Zhang, Yaming & Su, Yanyuan & Weigang, Li & Liu, Haiou, 2019. "Interacting model of rumor propagation and behavior spreading in multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 121(C), pages 168-177.
    10. Zhu, Yu-Xiao & Cao, Yan-Yan & Chen, Ting & Qiu, Xiao-Yan & Wang, Wei & Hou, Rui, 2018. "Crossover phenomena in growth pattern of social contagions with restricted contact," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 408-414.
    11. Zhou, Bin & Yan, Xiao-Yong & Xu, Xiao-Ke & Xu, Xiao-Ting & Wang, Nianxin, 2018. "Evolutionary of online social networks driven by pareto wealth distribution and bidirectional preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 427-434.
    12. Saxena, Chandni & Doja, M.N. & Ahmad, Tanvir, 2018. "Group based centrality for immunization of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 35-47.
    13. Kotnis, Bhushan & Kuri, Joy, 2016. "Cost effective campaigning in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 670-681.
    14. Dai, Hui & Wang, Xiaoyue & Lu, Yikang & Hou, Yunxiang & Shi, Lei, 2024. "The effect of intraspecific cooperation in a three-species cyclic predator-prey model," Applied Mathematics and Computation, Elsevier, vol. 470(C).
    15. Jose L Herrera & Ravi Srinivasan & John S Brownstein & Alison P Galvani & Lauren Ancel Meyers, 2016. "Disease Surveillance on Complex Social Networks," PLOS Computational Biology, Public Library of Science, vol. 12(7), pages 1-16, July.
    16. Ndii, Meksianis Z. & Adi, Yudi Ari, 2021. "Understanding the effects of individual awareness and vector controls on malaria transmission dynamics using multiple optimal control," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    17. Karikalan Nagarajan & Bharathidasan Palani & Javeed Basha & Lavanya Jayabal & Malaisamy Muniyandi, 2022. "A social networks-driven approach to understand the unique alcohol mixing patterns of tuberculosis patients: reporting methods and findings from a high TB-burden setting," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-8, December.
    18. Gong Kai & Kang Li, 2018. "A New K-Shell Decomposition Method for Identifying Influential Spreaders of Epidemics on Community Networks," Journal of Systems Science and Information, De Gruyter, vol. 6(4), pages 366-375, August.
    19. Baba, Isa Abdullahi & Kaymakamzade, Bilgen & Hincal, Evren, 2018. "Two-strain epidemic model with two vaccinations," Chaos, Solitons & Fractals, Elsevier, vol. 106(C), pages 342-348.
    20. Huo, Liang’an & Cheng, Yingying & Liu, Chen & Ding, Fan, 2018. "Dynamic analysis of rumor spreading model for considering active network nodes and nonlinear spreading rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 24-35.

    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:eee:chsofr:v:181:y:2024:i:c:s0960077924001371. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.