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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
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    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.
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