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Reconstructing antibody dynamics to estimate the risk of influenza virus infection

Author

Listed:
  • Tim K. Tsang

    (The University of Hong Kong
    Hong Kong Science and Technology Park)

  • Ranawaka A. P. M. Perera

    (The University of Hong Kong
    The University of Hong Kong)

  • Vicky J. Fang

    (The University of Hong Kong)

  • Jessica Y. Wong

    (The University of Hong Kong)

  • Eunice Y. Shiu

    (The University of Hong Kong)

  • Hau Chi So

    (The University of Hong Kong)

  • Dennis K. M. Ip

    (The University of Hong Kong)

  • J. S. Malik Peiris

    (The University of Hong Kong
    The University of Hong Kong)

  • Gabriel M. Leung

    (The University of Hong Kong
    Hong Kong Science and Technology Park)

  • Benjamin J. Cowling

    (The University of Hong Kong
    Hong Kong Science and Technology Park)

  • Simon Cauchemez

    (Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS)

Abstract

For >70 years, a 4-fold or greater rise in antibody titer has been used to confirm influenza virus infections in paired sera, despite recognition that this heuristic can lack sensitivity. Here we analyze with a novel Bayesian model a large cohort of 2353 individuals followed for up to 5 years in Hong Kong to characterize influenza antibody dynamics and develop an algorithm to improve the identification of influenza virus infections. After infection, we estimate that hemagglutination-inhibiting (HAI) titers were boosted by 16-fold on average and subsequently decrease by 14% per year. In six epidemics, the infection risks for adults were 3%–19% while the infection risks for children were 1.6–4.4 times higher than that of younger adults. Every two-fold increase in pre-epidemic HAI titer was associated with 19%–58% protection against infection. Our inferential framework clarifies the contributions of age and pre-epidemic HAI titers to characterize individual infection risk.

Suggested Citation

  • Tim K. Tsang & Ranawaka A. P. M. Perera & Vicky J. Fang & Jessica Y. Wong & Eunice Y. Shiu & Hau Chi So & Dennis K. M. Ip & J. S. Malik Peiris & Gabriel M. Leung & Benjamin J. Cowling & Simon Caucheme, 2022. "Reconstructing antibody dynamics to estimate the risk of influenza virus infection," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29310-8
    DOI: 10.1038/s41467-022-29310-8
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    References listed on IDEAS

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    1. Henrik Salje & Derek A. T. Cummings & Isabel Rodriguez-Barraquer & Leah C. Katzelnick & Justin Lessler & Chonticha Klungthong & Butsaya Thaisomboonsuk & Ananda Nisalak & Alden Weg & Damon Ellison & Lo, 2018. "Reconstruction of antibody dynamics and infection histories to evaluate dengue risk," Nature, Nature, vol. 557(7707), pages 719-723, May.
    2. Sigrid Gouma & Kangchon Kim & Madison E. Weirick & Megan E. Gumina & Angela Branche & David J. Topham & Emily T. Martin & Arnold S. Monto & Sarah Cobey & Scott E. Hensley, 2020. "Middle-aged individuals may be in a perpetual state of H3N2 influenza virus susceptibility," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    3. Joël Mossong & Niel Hens & Mark Jit & Philippe Beutels & Kari Auranen & Rafael Mikolajczyk & Marco Massari & Stefania Salmaso & Gianpaolo Scalia Tomba & Jacco Wallinga & Janneke Heijne & Malgorzata Sa, 2008. "Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases," PLOS Medicine, Public Library of Science, vol. 5(3), pages 1-1, March.
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