IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0313037.html
   My bibliography  Save this article

Sorting out assortativity: When can we assess the contributions of different population groups to epidemic transmission?

Author

Listed:
  • Cyril Geismar
  • Peter J White
  • Anne Cori
  • Thibaut Jombart

Abstract

Characterising the transmission dynamics between various population groups is critical for implementing effective outbreak control measures whilst minimising financial costs and societal disruption. While recent technological and methodological advances have made individual-level transmission chain data increasingly available, it remains unclear how effectively this data can inform group-level transmission patterns, particularly in small, rapidly saturating outbreak settings. We introduce a novel framework that leverages transmission chain data to estimate group transmission assortativity; this quantifies the extent to which individuals transmit within their own group compared to others. Through extensive simulations mimicking nosocomial outbreaks, we assessed the conditions under which our estimator performs effectively and established guidelines for minimal data requirements in small outbreak settings where saturation may occur rapidly. Notably, we demonstrate that detecting and quantifying transmission assortativity is most reliable when at least 30 cases have been observed in each group, before reaching their respective epidemic peaks.

Suggested Citation

  • Cyril Geismar & Peter J White & Anne Cori & Thibaut Jombart, 2024. "Sorting out assortativity: When can we assess the contributions of different population groups to epidemic transmission?," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-13, December.
    • Handle: RePEc:plo:pone00:0313037
    DOI: 10.1371/journal.pone.0313037
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0313037
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0313037&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0313037?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
    ---><---

    References listed on IDEAS

    as
    1. Badham, Jennifer & Stocker, Rob, 2010. "The impact of network clustering and assortativity on epidemic behaviour," Theoretical Population Biology, Elsevier, vol. 77(1), pages 71-75.
    2. Benjamin B. Lindsey & Ch. Julián Villabona-Arenas & Finlay Campbell & Alexander J. Keeley & Matthew D. Parker & Dhruv R. Shah & Helena Parsons & Peijun Zhang & Nishchay Kakkar & Marta Gallis & Benjami, 2022. "Characterising within-hospital SARS-CoV-2 transmission events using epidemiological and viral genomic data across two pandemic waves," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    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. Shams, Bita & Khansari, Mohammad, 2015. "On the impact of epidemic severity on network immunization algorithms," Theoretical Population Biology, Elsevier, vol. 106(C), pages 83-93.
    2. Li, Xun & Cao, Lang, 2016. "Diffusion processes of fragmentary information on scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 624-634.
    3. Li, Yinwei & Jiang, Guo-Ping & Wu, Meng & Song, Yu-Rong & Wang, Haiyan, 2021. "Undirected Congruence Model: Topological characteristics and epidemic spreading," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    4. repec:plo:pone00:0219361 is not listed on IDEAS
    5. Sheryl Le Chang & Mahendra Piraveenan & Mikhail Prokopenko, 2019. "The Effects of Imitation Dynamics on Vaccination Behaviours in SIR-Network Model," IJERPH, MDPI, vol. 16(14), pages 1-31, July.
    6. Duncan, A.J. & Gunn, G.J. & Umstatter, C. & Humphry, R.W., 2014. "Replicating disease spread in empirical cattle networks by adjusting the probability of infection in random networks," Theoretical Population Biology, Elsevier, vol. 98(C), pages 11-18.
    7. Nunner, Hendrik & Buskens, Vincent & Teslya, Alexandra & Kretzschmar, Mirjam, 2022. "Health behavior homophily can mitigate the spread of infectious diseases in small-world networks," Social Science & Medicine, Elsevier, vol. 312(C).
    8. Muller, Eitan & Peres, Renana, 2019. "The effect of social networks structure on innovation performance: A review and directions for research," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 3-19.
    9. Jeong, Wonhee & Yu, Unjong, 2022. "Effects of quadrilateral clustering on complex contagion," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    10. Chen, Jie & Tan, Xuegang & Cao, Jinde & Li, Ming, 2022. "Effect of coupling structure on traffic-driven epidemic spreading in interconnected networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    11. Yeftanus Antonio & Sapto Wahyu Indratno & Suhadi Wido Saputro, 2021. "Pricing of cyber insurance premiums using a Markov-based dynamic model with clustering structure," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-28, October.

    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:plo:pone00:0313037. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.