IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v159y2022ics0960077922003101.html
   My bibliography  Save this article

Impact of hopping characteristics of inter-layer commuters on epidemic spreading in multilayer networks

Author

Listed:
  • Wu, Dayu
  • Liu, Ying
  • Tang, Ming
  • Xu, Xiao-Ke
  • Guan, Shuguang

Abstract

People travel frequently between communities, and the movement of population affects the spreading dynamics of epidemic and information within/between communities. Here we establish a two-layer network where the inter-layer coupling is induced by the movement of individuals between layers, and study the impact of the travellers' hopping preference and activity on the epidemic spreading dynamics. Through large numerical simulations and theoretical analysis on synthesized networks and empirical networks, we find that travellers' hopping preference for different layers has opposite effects on the spreading dynamics within layers and the coupling between layers, resulting in interesting non-monotonic changes in the epidemic threshold and spreading coverage. On the contrary, the impact of travellers' hopping activity on the spreading of epidemic displays a monotonous trend. Based on quenched mean-field approximation, we proposed a theoretical framework that can accurately describe the spreading dynamics in two-layer networks with inter-layer hopping. Our research provides a method and theoretical framework to describe real-world scenarios where human movement and epidemic spreading coevolve.

Suggested Citation

  • Wu, Dayu & Liu, Ying & Tang, Ming & Xu, Xiao-Ke & Guan, Shuguang, 2022. "Impact of hopping characteristics of inter-layer commuters on epidemic spreading in multilayer networks," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    • Handle: RePEc:eee:chsofr:v:159:y:2022:i:c:s0960077922003101
    DOI: 10.1016/j.chaos.2022.112100
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077922003101
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2022.112100?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. Sergey V. Buldyrev & Roni Parshani & Gerald Paul & H. Eugene Stanley & Shlomo Havlin, 2010. "Catastrophic cascade of failures in interdependent networks," Nature, Nature, vol. 464(7291), pages 1025-1028, April.
    2. Mi Feng & Shi-Min Cai & Ming Tang & Ying-Cheng Lai, 2019. "Publisher Correction: Equivalence and its invalidation between non-Markovian and Markovian spreading dynamics on complex networks," Nature Communications, Nature, vol. 10(1), pages 1-2, December.
    3. Mi Feng & Shi-Min Cai & Ming Tang & Ying-Cheng Lai, 2019. "Equivalence and its invalidation between non-Markovian and Markovian spreading dynamics on complex networks," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    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. Li, Wenjie & Ji, Jinchen & Huang, Lihong & Zhang, Ying, 2023. "Complex dynamics and impulsive control of a chemostat model under the ratio threshold policy," Chaos, Solitons & Fractals, Elsevier, vol. 167(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. Zhu, Yanpeng & Chen, Lei & Jia, Chun-Xiao & Meng, Fanyuan & Liu, Run-Ran, 2023. "Non-Markovian node fragility in cascading failures on random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    2. Basnarkov, Lasko & Tomovski, Igor & Sandev, Trifce & Kocarev, Ljupco, 2022. "Non-Markovian SIR epidemic spreading model of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    3. Wang, Chengjiang & Wang, Li & Wang, Juan & Sun, Shiwen & Xia, Chengyi, 2017. "Inferring the reputation enhances the cooperation in the public goods game on interdependent lattices," Applied Mathematics and Computation, Elsevier, vol. 293(C), pages 18-29.
    4. Tang, Liang & Jing, Ke & He, Jie & Stanley, H. Eugene, 2016. "Robustness of assembly supply chain networks by considering risk propagation and cascading failure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 459(C), pages 129-139.
    5. Shogo Mizutaka & Kousuke Yakubo, 2017. "Structural instability of large-scale functional networks," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-11, July.
    6. Balint, T. & Lamperti, F. & Mandel, A. & Napoletano, M. & Roventini, A. & Sapio, A., 2017. "Complexity and the Economics of Climate Change: A Survey and a Look Forward," Ecological Economics, Elsevier, vol. 138(C), pages 252-265.
    7. Fridgen, Gilbert & Keller, Robert & Körner, Marc-Fabian & Schöpf, Michael, 2020. "A holistic view on sector coupling," Energy Policy, Elsevier, vol. 147(C).
    8. Hernandez-Fajardo, Isaac & Dueñas-Osorio, Leonardo, 2013. "Probabilistic study of cascading failures in complex interdependent lifeline systems," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 260-272.
    9. Yu, Haitao & Wang, Jiang & Liu, Chen & Deng, Bin & Wei, Xile, 2014. "Delay-induced synchronization transitions in modular scale-free neuronal networks with hybrid electrical and chemical synapses," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 25-34.
    10. Sgrignoli, Paolo & Metulini, Rodolfo & Schiavo, Stefano & Riccaboni, Massimo, 2015. "The relation between global migration and trade networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 245-260.
    11. Zhou, Yaoming & Wang, Junwei, 2018. "Efficiency of complex networks under failures and attacks: A percolation approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 658-664.
    12. Gong, Chang & Li, Jichao & Qian, Liwei & Li, Siwei & Yang, Zhiwei & Yang, Kewei, 2024. "HMSL: Source localization based on higher-order Markov propagation," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    13. Monsalve, Mauricio & de la Llera, Juan Carlos, 2019. "Data-driven estimation of interdependencies and restoration of infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 167-180.
    14. Liu, Huan & Tatano, Hirokazu & Pflug, Georg & Hochrainer-Stigler, Stefan, 2021. "Post-disaster recovery in industrial sectors: A Markov process analysis of multiple lifeline disruptions," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    15. Liu, Run-Ran & Chu, Changchang & Meng, Fanyuan, 2023. "Higher-order interdependent percolation on hypergraphs," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    16. Krawiecki, A., 2018. "Spin glass transition in a simple variant of the Ising model on multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 773-790.
    17. Shekhtman, Louis M. & Danziger, Michael M. & Havlin, Shlomo, 2016. "Recent advances on failure and recovery in networks of networks," Chaos, Solitons & Fractals, Elsevier, vol. 90(C), pages 28-36.
    18. Weihua Lei & Luiz G. A. Alves & Luís A. Nunes Amaral, 2022. "Forecasting the evolution of fast-changing transportation networks using machine learning," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    19. Wang, Gang-Jin & Chen, Yang-Yang & Si, Hui-Bin & Xie, Chi & Chevallier, Julien, 2021. "Multilayer information spillover networks analysis of China’s financial institutions based on variance decompositions," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 325-347.
    20. Leto Peel & Tiago P. Peixoto & Manlio De Domenico, 2022. "Statistical inference links data and theory in network science," Nature Communications, Nature, vol. 13(1), pages 1-15, December.

    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:159:y:2022:i:c:s0960077922003101. 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.