IDEAS home Printed from https://ideas.repec.org/a/spr/eurphb/v97y2024i5d10.1140_epjb_s10051-024-00675-7.html
   My bibliography  Save this article

Rapid disease spread on dense networks with power-law topology

Author

Listed:
  • J. J. Esquivel-Gómez

    (Instituto Potosino de Investigación Científica y Tecnológica (IPICYT))

  • J. G. Barajas-Ramírez

    (Instituto Potosino de Investigación Científica y Tecnológica (IPICYT))

Abstract

Models of disease spread in networks typically focus on exploring various measures to reduce the spread of disease across individuals within a network. However, the topology of the underlying network plays an important role in determining the best time to implement mitigation measures to achieve better results. In this article we show the behavior of the well-known SIR (susceptible-infected-removed) and SIS (susceptible-infected-susceptible) models over networks with both scale-free and dense structure with power-law topology $$P(k)\sim k^{-\zeta }$$ P ( k ) ∼ k - ζ with $$1

Suggested Citation

  • J. J. Esquivel-Gómez & J. G. Barajas-Ramírez, 2024. "Rapid disease spread on dense networks with power-law topology," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 97(5), pages 1-10, May.
  • Handle: RePEc:spr:eurphb:v:97:y:2024:i:5:d:10.1140_epjb_s10051-024-00675-7
    DOI: 10.1140/epjb/s10051-024-00675-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1140/epjb/s10051-024-00675-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1140/epjb/s10051-024-00675-7?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. Huayan Pei & Guanghui Yan & Yaning Huang, 2023. "Impact of contact rate on epidemic spreading in complex networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(4), pages 1-7, April.
    2. Xiao-Jie Li & Xiang Li, 2020. "Vaccinating SIS epidemics under evolving perception in heterogeneous networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 93(10), pages 1-7, October.
    3. Yutaka Okabe & Akira Shudo, 2021. "Microscopic Numerical Simulations of Epidemic Models on Networks," Mathematics, MDPI, vol. 9(9), pages 1-19, April.
    4. Zhidong He & Piet Van Mieghem, 2018. "The fastest spreader in SIS epidemics on networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 91(5), pages 1-8, May.
    5. H. Jeong & B. Tombor & R. Albert & Z. N. Oltvai & A.-L. Barabási, 2000. "The large-scale organization of metabolic networks," Nature, Nature, vol. 407(6804), pages 651-654, October.
    6. Nekovee, M. & Moreno, Y. & Bianconi, G. & Marsili, M., 2007. "Theory of rumour spreading in complex social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 374(1), pages 457-470.
    7. D. Garlaschelli & M. I. Loffredo, 2005. "Structure and Evolution of the World Trade Network," Papers physics/0502066, arXiv.org, revised May 2005.
    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. Yin, Mei & Zhu, Lingjiong, 2016. "Reciprocity in directed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 71-84.
    2. Lv, Xijian & Fan, Dongmei & Yang, Junxian & Li, Qiang & Zhou, Li, 2024. "Delay differential equation modeling of social contagion with higher-order interactions," Applied Mathematics and Computation, Elsevier, vol. 466(C).
    3. Merza, Ádám & London, András & Kiss, István Márton & Pelle, Anita & Dombi, József & Németh, Tamás, 2016. "A világkereskedelem hálózatelméleti vizsgálatának lehetőségeiről [The scope for analysis of world trade through network theory]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(1), pages 79-98.
    4. Jin Wang & Bo Huang & Xuefeng Xia & Zhirong Sun, 2006. "Funneled Landscape Leads to Robustness of Cell Networks: Yeast Cell Cycle," PLOS Computational Biology, Public Library of Science, vol. 2(11), pages 1-10, November.
    5. Hosni, Adil Imad Eddine & Li, Kan & Ahmad, Sadique, 2020. "Analysis of the impact of online social networks addiction on the propagation of rumors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    6. Marco Dueñas & Giorgio Fagiolo, 2013. "Modeling the International-Trade Network: a gravity approach," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 155-178, April.
    7. Jorge Peña & Yannick Rochat, 2012. "Bipartite Graphs as Models of Population Structures in Evolutionary Multiplayer Games," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-13, September.
    8. Cai, Xiaomei & Liu, Chan & Zheng, Shuxian & Hu, Han & Tan, Zhanglu, 2023. "Analysis on the evolution characteristics of barite international trade pattern based on complex networks," Resources Policy, Elsevier, vol. 83(C).
    9. Jia, Pingqi & Wang, Chao & Zhang, Gaoyu & Ma, Jianfeng, 2019. "A rumor spreading model based on two propagation channels in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 342-353.
    10. Hao, Xiaoqing & An, Haizhong & Qi, Hai & Gao, Xiangyun, 2016. "Evolution of the exergy flow network embodied in the global fossil energy trade: Based on complex network," Applied Energy, Elsevier, vol. 162(C), pages 1515-1522.
    11. Xuefeng Yue & Liangan Huo, 2022. "Analysis of the Stability and Optimal Control Strategy for an ISCR Rumor Propagation Model with Saturated Incidence and Time Delay on a Scale-Free Network," Mathematics, MDPI, vol. 10(20), pages 1-20, October.
    12. Kitamura, Toshihiko & Managi, Shunsuke, 2017. "Driving force and resistance: Network feature in oil trade," Applied Energy, Elsevier, vol. 208(C), pages 361-375.
    13. Zan, Yongli & Wu, Jianliang & Li, Ping & Yu, Qinglin, 2014. "SICR rumor spreading model in complex networks: Counterattack and self-resistance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 159-170.
    14. 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.
    15. Fink, Christian G. & Fullin, Kelly & Gutierrez, Guillermo & Omodt, Nathan & Zinnecker, Sydney & Sprint, Gina & McCulloch, Sean, 2023. "A centrality measure for quantifying spread on weighted, directed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    16. Florian Blöchl & Fabian J. Theis & Fernando Vega-Redondo & Eric O'N. Fisher, 2010. "Which Sectors of a Modern Economy are most Central?," CESifo Working Paper Series 3175, CESifo.
    17. Jianhong Chen & Hongcai Ma & Shan Yang, 2023. "SEIOR Rumor Propagation Model Considering Hesitating Mechanism and Different Rumor-Refuting Ways in Complex Networks," Mathematics, MDPI, vol. 11(2), pages 1-22, January.
    18. Tamás Nepusz & Tamás Vicsek, 2013. "Hierarchical Self-Organization of Non-Cooperating Individuals," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-9, December.
    19. Bartesaghi, Paolo & Clemente, Gian Paolo & Grassi, Rosanna & Luu, Duc Thi, 2022. "The multilayer architecture of the global input-output network and its properties," Journal of Economic Behavior & Organization, Elsevier, vol. 204(C), pages 304-341.
    20. Aslam, Faheem & Aziz, Saqib & Nguyen, Duc Khuong & Mughal, Khurrum S. & Khan, Maaz, 2020. "On the efficiency of foreign exchange markets in times of the COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 161(C).

    More about this item

    Statistics

    Access and download statistics

    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:spr:eurphb:v:97:y:2024:i:5:d:10.1140_epjb_s10051-024-00675-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.