IDEAS home Printed from https://ideas.repec.org/a/spr/eurphb/v96y2023i4d10.1140_epjb_s10051-023-00513-2.html
   My bibliography  Save this article

Impact of contact rate on epidemic spreading in complex networks

Author

Listed:
  • Huayan Pei

    (Lanzhou Jiaotong University
    Key Laboratory of Media Convergence Technology and Communication)

  • Guanghui Yan

    (Lanzhou Jiaotong University
    Key Laboratory of Media Convergence Technology and Communication)

  • Yaning Huang

    (Key Laboratory of Media Convergence Technology and Communication
    Gansu Daily Newspaper Industry Group)

Abstract

Contact reduction is an effective strategy to mitigate the spreading of epidemic. However, the existing reaction–diffusion equations for infectious disease are unable to characterize this effect. Thus, we here propose an extended susceptible-infected-recovered model by incorporating contact rate into the standard SIR model, and concentrate on investigating its impact on epidemic transmission. We analytically derive the epidemic thresholds on homogeneous and heterogeneous networks, respectively. The effects of contact rate on spreading speed, scale and outbreak threshold are explored on ER and SF networks. Simulations results show that epidemic dissemination is significantly mitigated when contact rate is reduced. Importantly, epidemic spreads faster on heterogeneous networks while broader on homogeneous networks, and the outbreak thresholds of the former are smaller. Graphical abstract

Suggested Citation

  • 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.
  • Handle: RePEc:spr:eurphb:v:96:y:2023:i:4:d:10.1140_epjb_s10051-023-00513-2
    DOI: 10.1140/epjb/s10051-023-00513-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1140/epjb/s10051-023-00513-2
    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/s10051-023-00513-2?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. Jeffrey E. Harris, 2020. "The Subways Seeded the Massive Coronavirus Epidemic in New York City," NBER Working Papers 27021, National Bureau of Economic Research, Inc.
    2. 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.
    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. 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).
    2. 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.

    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. Jeffrey E. Harris, 2020. "Geospatial Analysis of the September 2020 Coronavirus Outbreak at the University of Wisconsin – Madison: Did a Cluster of Local Bars Play a Critical Role?," NBER Working Papers 28132, National Bureau of Economic Research, Inc.
    2. 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).
    3. de Palma, André & Vosough, Shaghayegh & Liao, Feixiong, 2022. "An overview of effects of COVID-19 on mobility and lifestyle: 18 months since the outbreak," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 372-397.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    9. 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.
    10. Chad Cotti & Bryan Engelhardt & Joshua Foster & Erik Nesson & Paul Niekamp, 2021. "The relationship between in‐person voting and COVID‐19: Evidence from the Wisconsin primary," Contemporary Economic Policy, Western Economic Association International, vol. 39(4), pages 760-777, October.
    11. Borsati, Mattia & Nocera, Silvio & Percoco, Marco, 2022. "Questioning the spatial association between the initial spread of COVID-19 and transit usage in Italy," Research in Transportation Economics, Elsevier, vol. 95(C).
    12. Andrés Gómez-Lobo & Mauro Gutiérrez & Sandro Huamaní & Diego Marino & Tomás Serebrisky & Ben Solís, 2024. "Access to water and COVID-19: a regression discontinuity analysis for the peri-urban areas of metropolitan Lima, Peru," Water International, Taylor & Francis Journals, vol. 49(1), pages 52-79, January.
    13. Matthias Flückiger & Markus Ludwig, 2023. "Spatial networks and the spread of COVID-19: results and policy implications from Germany," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 43(1), pages 1-27, April.
    14. Huo, Liang’an & Jiang, Jiehui & Gong, Sixing & He, Bing, 2016. "Dynamical behavior of a rumor transmission model with Holling-type II functional response in emergency event," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 228-240.
    15. 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.
    16. Nizamani, Sarwat & Memon, Nasrullah & Galam, Serge, 2014. "From public outrage to the burst of public violence: An epidemic-like model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 620-630.
    17. 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.
    18. Qian, Zhen & Tang, Shaoting & Zhang, Xiao & Zheng, Zhiming, 2015. "The independent spreaders involved SIR Rumor model in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 95-102.
    19. Guilherme Ferraz de Arruda & Lucas G. S. Jeub & Angélica S. Mata & Francisco A. Rodrigues & Yamir Moreno, 2022. "From subcritical behavior to a correlation-induced transition in rumor models," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    20. Laroze, Denise & Neumayer, Eric & Plümper, Thomas, 2021. "COVID-19 does not stop at open borders: Spatial contagion among local authority districts during England's first wave," Social Science & Medicine, Elsevier, vol. 270(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:96:y:2023:i:4:d:10.1140_epjb_s10051-023-00513-2. 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.