IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-30639-3.html
   My bibliography  Save this article

Non-selective distribution of infectious disease prevention may outperform risk-based targeting

Author

Listed:
  • Benjamin Steinegger

    (Universitat Rovira i Virgili)

  • Iacopo Iacopini

    (Central European University
    Aix Marseille Univ, Université de Toulon, CNRS, CPT)

  • Andreia Sofia Teixeira

    (LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa
    INESC-ID)

  • Alberto Bracci

    (University of London)

  • Pau Casanova-Ferrer

    (Grupo Interdisciplinar de Sistemas Complejos (GISC), Department of Mathematics, Carlos III University of Madrid
    Centro Nacional de Biotecnología, CNB-CSIC)

  • Alberto Antonioni

    (Grupo Interdisciplinar de Sistemas Complejos (GISC), Department of Mathematics, Carlos III University of Madrid)

  • Eugenio Valdano

    (Sorbonne Université, INSERM, Institut Pierre Louis d’Epidémiologie et de Santé Publique)

Abstract

Epidemic control often requires optimal distribution of available vaccines and prophylactic tools, to protect from infection those susceptible. Well-established theory recommends prioritizing those at the highest risk of exposure. But the risk is hard to estimate, especially for diseases involving stigma and marginalization. We address this conundrum by proving that one should target those at high risk only if the infection-averting efficacy of prevention is above a critical value, which we derive analytically. We apply this to the distribution of pre-exposure prophylaxis (PrEP) of the Human Immunodeficiency Virus (HIV) among men-having-sex-with-men (MSM), a population particularly vulnerable to HIV. PrEP is effective in averting infections, but its global scale-up has been slow, showing the need to revisit distribution strategies, currently risk-based. Using data from MSM communities in 58 countries, we find that non-selective PrEP distribution often outperforms risk-based, showing that a logistically simpler strategy is also more effective. Our theory may help design more feasible and successful prevention.

Suggested Citation

  • Benjamin Steinegger & Iacopo Iacopini & Andreia Sofia Teixeira & Alberto Bracci & Pau Casanova-Ferrer & Alberto Antonioni & Eugenio Valdano, 2022. "Non-selective distribution of infectious disease prevention may outperform risk-based targeting," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30639-3
    DOI: 10.1038/s41467-022-30639-3
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-30639-3
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-30639-3?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. Petter Holme & Nelly Litvak, 2017. "Cost-efficient vaccination protocols for network epidemiology," PLOS Computational Biology, Public Library of Science, vol. 13(9), pages 1-18, September.
    2. Lilith K Whittles & Peter J White & Xavier Didelot, 2019. "A dynamic power-law sexual network model of gonorrhoea outbreaks," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-20, March.
    3. Samuel F Rosenblatt & Jeffrey A Smith & G Robin Gauthier & Laurent Hébert-Dufresne, 2020. "Immunization strategies in networks with missing data," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-21, July.
    4. Saeed Osat & Ali Faqeeh & Filippo Radicchi, 2017. "Optimal percolation on multiplex networks," Nature Communications, Nature, vol. 8(1), pages 1-7, 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. Fan, Dongming & Sun, Bo & Dui, Hongyan & Zhong, Jilong & Wang, Ziyao & Ren, Yi & Wang, Zili, 2022. "A modified connectivity link addition strategy to improve the resilience of multiplex networks against attacks," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    2. Liang, Yuan & Qi, Mingze & Huangpeng, Qizi & Duan, Xiaojun, 2023. "Percolation of interlayer feature-correlated multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    3. Le Song & Guilong Zhu & Xiao Yin, 2024. "Evaluating the wisdom of scholar crowds from the perspective of knowledge diffusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(9), pages 5103-5139, September.
    4. Tian, Yang & Tian, Hui & Cui, Qimei & Zhu, Xuzhen, 2024. "Phase transition phenomena in social propagation with dynamic fashion tendency and individual contact," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    5. Qi, Mingze & Tan, Suoyi & Chen, Peng & Duan, Xiaojun & Lu, Xin, 2023. "Efficient network intervention with sampling information," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    6. Song, Le & Ma, Yinghong, 2022. "Evaluating tacit knowledge diffusion with algebra matrix algorithm based social networks," Applied Mathematics and Computation, Elsevier, vol. 428(C).
    7. Samuel P. C. Brand & Massimo Cavallaro & Fergus Cumming & Charlie Turner & Isaac Florence & Paula Blomquist & Joe Hilton & Laura M. Guzman-Rincon & Thomas House & D. James Nokes & Matt J. Keeling, 2023. "The role of vaccination and public awareness in forecasts of Mpox incidence in the United Kingdom," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    8. Wang, Yichao & Tu, Lilan & Wang, Xianjia & Guo, Yifei, 2024. "Evolutionary vaccination game considering intra-seasonal strategy shifts regarding multi-seasonal epidemic spreading," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    9. Jamie Bedson & Laura A. Skrip & Danielle Pedi & Sharon Abramowitz & Simone Carter & Mohamed F. Jalloh & Sebastian Funk & Nina Gobat & Tamara Giles-Vernick & Gerardo Chowell & João Rangel Almeida & Ran, 2021. "A review and agenda for integrated disease models including social and behavioural factors," Nature Human Behaviour, Nature, vol. 5(7), pages 834-846, July.
    10. Natalia Markovich, 2024. "Extremal properties of evolving networks: local dependence and heavy tails," Annals of Operations Research, Springer, vol. 339(3), pages 1839-1870, August.
    11. Quan Ye & Guanghui Yan & Wenwen Chang & Hao Luo, 2023. "Vital node identification based on cycle structure in a multiplex network," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(2), pages 1-16, February.
    12. Wang, Ning & Jin, Zi-Yang & Zhao, Jiao, 2021. "Cascading failures of overload behaviors on interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    13. Zhao, Dawei & Wang, Lianhai & Xu, Shujiang & Liu, Guangqi & Han, Xiaohui & Li, Shudong, 2017. "Vital layer nodes of multiplex networks for immunization and attack," Chaos, Solitons & Fractals, Elsevier, vol. 105(C), pages 169-175.
    14. Thomas Parmer & Luis M. Rocha & Filippo Radicchi, 2022. "Influence maximization in Boolean networks," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    15. Osat, Saeed & Radicchi, Filippo, 2018. "Observability transition in multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 745-761.

    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:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30639-3. 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.nature.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.