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Seasonal antigenic prediction of influenza A H3N2 using machine learning

Author

Listed:
  • Syed Awais W. Shah

    (Clear Water Bay)

  • Daniel P. Palomar

    (Clear Water Bay
    Clear Water Bay)

  • Ian Barr

    (WHO Collaborating Centre for Reference and Research on Influenza
    at The Peter Doherty Institute for Infection and Immunity)

  • Leo L. M. Poon

    (The University of Hong Kong
    Centre for Immunology & Infection)

  • Ahmed Abdul Quadeer

    (Clear Water Bay
    University of Melbourne)

  • Matthew R. McKay

    (at The Peter Doherty Institute for Infection and Immunity
    University of Melbourne)

Abstract

Antigenic characterization of circulating influenza A virus (IAV) isolates is routinely assessed by using the hemagglutination inhibition (HI) assays for surveillance purposes. It is also used to determine the need for annual influenza vaccine updates as well as for pandemic preparedness. Performing antigenic characterization of IAV on a global scale is confronted with high costs, animal availability, and other practical challenges. Here we present a machine learning model that accurately predicts (normalized) outputs of HI assays involving circulating human IAV H3N2 viruses, using their hemagglutinin subunit 1 (HA1) sequences and associated metadata. Each season, the model learns an updated nonlinear mapping of genetic to antigenic changes using data from past seasons only. The model accurately distinguishes antigenic variants from non-variants and adaptively characterizes seasonal dynamics of HA1 sites having the strongest influence on antigenic change. Antigenic predictions produced by the model can aid influenza surveillance, public health management, and vaccine strain selection activities.

Suggested Citation

  • Syed Awais W. Shah & Daniel P. Palomar & Ian Barr & Leo L. M. Poon & Ahmed Abdul Quadeer & Matthew R. McKay, 2024. "Seasonal antigenic prediction of influenza A H3N2 using machine learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47862-9
    DOI: 10.1038/s41467-024-47862-9
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    References listed on IDEAS

    as
    1. Nicholas C. Wu & Jakub Otwinowski & Andrew J. Thompson & Corwin M. Nycholat & Armita Nourmohammad & Ian A. Wilson, 2020. "Major antigenic site B of human influenza H3N2 viruses has an evolving local fitness landscape," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    2. Nicholas C. Wu & Andrew J. Thompson & Jia Xie & Chih-Wei Lin & Corwin M. Nycholat & Xueyong Zhu & Richard A. Lerner & James C. Paulson & Ian A. Wilson, 2018. "A complex epistatic network limits the mutational reversibility in the influenza hemagglutinin receptor-binding site," Nature Communications, Nature, vol. 9(1), pages 1-13, December.
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