IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v579y2021ics0378437121003927.html
   My bibliography  Save this article

Epidemic spreading with awareness on multi-layer activity-driven networks

Author

Listed:
  • Jia, Mengqi
  • Li, Xin
  • Ding, Li

Abstract

Network structure plays an important role in epidemic spreading process. In this paper, we propose a susceptible–alert–infected–susceptible epidemic spreading model based on coupled activity-driven networks. The critical conditions for epidemic outbreaks are analyzed based on the proposed model. The explicit expressions of the critical conditions are obtained, which are determined by the multi-layer activity-driven network structure associated with propagation parameters. We also analyze the influence of network structure and propagation parameters on the epidemic outbreak critical condition, which may shed some lights on the control of the epidemic spreading in future. The correctness of the theoretical results is corroborated by Monte Carlo simulations.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:579:y:2021:i:c:s0378437121003927
    DOI: 10.1016/j.physa.2021.126119
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437121003927
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2021.126119?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. Petter Holme, 2015. "Modern temporal network theory: a colloquium," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 88(9), pages 1-30, September.
    2. Li Ding & Ping Hu, 2019. "Contagion Processes on Time-Varying Networks with Homophily-Driven Group Interactions," Complexity, Hindawi, vol. 2019, pages 1-13, October.
    3. Lu, Yonglei & Liu, Jing, 2019. "The impact of information dissemination strategies to epidemic spreading on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    4. Chen, Xiaolong & Gong, Kai & Wang, Ruijie & Cai, Shimin & Wang, Wei, 2020. "Effects of heterogeneous self-protection awareness on resource-epidemic coevolution dynamics," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    5. 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).
    6. K. M. Ariful Kabir & Jun Tanimotoc, 2019. "Impact of awareness in metapopulation epidemic model to suppress the infected individuals for different graphs," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 92(9), pages 1-16, September.
    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. 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.
    2. 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.

    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. Dantsuji, Takao & Sugishita, Kashin & Fukuda, Daisuke, 2023. "Understanding changes in travel patterns during the COVID-19 outbreak in the three major metropolitan areas of Japan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    2. Pietro DeLellis & Anna DiMeglio & Franco Garofalo & Francesco Lo Iudice, 2017. "The evolving cobweb of relations among partially rational investors," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-21, February.
    3. 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).
    4. Karan, Rituraj & Biswal, Bibhu, 2017. "A model for evolution of overlapping community networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 380-390.
    5. Li, Mingwu & Dankowicz, Harry, 2019. "Impact of temporal network structures on the speed of consensus formation in opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1355-1370.
    6. Sindhuja Ranganathan & Mikko Kivelä & Juho Kanniainen, 2018. "Dynamics of investor spanning trees around dot-com bubble," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-14, June.
    7. 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).
    8. Zhu, He & Ma, Jing, 2018. "Knowledge diffusion in complex networks by considering time-varying information channels," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 225-235.
    9. Tao, Li & Kong, Shengzhou & He, Langzhou & Zhang, Fan & Li, Xianghua & Jia, Tao & Han, Zhen, 2022. "A sequential-path tree-based centrality for identifying influential spreaders in temporal networks," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    10. Mitja Steinbacher & Matthias Raddant & Fariba Karimi & Eva Camacho Cuena & Simone Alfarano & Giulia Iori & Thomas Lux, 2021. "Advances in the agent-based modeling of economic and social behavior," SN Business & Economics, Springer, vol. 1(7), pages 1-24, July.
    11. Chen, Xiaolong & Gong, Kai & Wang, Ruijie & Cai, Shimin & Wang, Wei, 2020. "Effects of heterogeneous self-protection awareness on resource-epidemic coevolution dynamics," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    12. Fei Wang & Zhenfang Zhu & Peiyu Liu & Peipei Wang, 2019. "Influence Maximization in Social Network Considering Memory Effect and Social Reinforcement Effect," Future Internet, MDPI, vol. 11(4), pages 1-16, April.
    13. Li, Huichun & Zhang, Xue & Zhao, Chengli, 2021. "Explaining social events through community evolution on temporal networks," Applied Mathematics and Computation, Elsevier, vol. 404(C).
    14. Liu, Jiawei & Ding, Jie, 2020. "Requesting for retweeting or donating? A research on how the fundraiser seeks help in the social charitable crowdfunding," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    15. Neil Hwang & Jiarui Xu & Shirshendu Chatterjee & Sharmodeep Bhattacharyya, 2022. "The Bethe Hessian and Information Theoretic Approaches for Online Change-Point Detection in Network Data," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 283-320, June.
    16. Cui, Guang-Hai & Wang, Zhen & Li, Jun-Li & Jin, Xing & Zhang, Zhi-Wang, 2021. "Influence of precaution and dynamic post-indemnity based insurance policy on controlling the propagation of epidemic security risks in networks," Applied Mathematics and Computation, Elsevier, vol. 392(C).
    17. Zhang, Mingli & Qin, Simeng & Zhu, Xiaoxia, 2021. "Information diffusion under public crisis in BA scale-free network based on SEIR model — Taking COVID-19 as an example," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    18. Zhang, Ningbo & Yang, Qiwen & Zhu, Xuzhen, 2022. "The impact of social resource allocation on epidemic transmission in complex networks," Applied Mathematics and Computation, Elsevier, vol. 433(C).
    19. Liu, Chuang & Zhou, Nan & Zhan, Xiu-Xiu & Sun, Gui-Quan & Zhang, Zi-Ke, 2020. "Markov-based solution for information diffusion on adaptive social networks," Applied Mathematics and Computation, Elsevier, vol. 380(C).
    20. Jordan Cambe & Sebastian Grauwin & Patrick Flandrin & Pablo Jensen, 2022. "A new clustering method to explore the dynamics of research communities," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4459-4482, August.

    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:phsmap:v:579:y:2021:i:c:s0378437121003927. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.