Seasonal antigenic prediction of influenza A H3N2 using machine learning.

Journal: Nature communications
PMID: 38714654

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.

Authors

  • Syed Awais W Shah
    Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China.
  • Daniel P Palomar
    Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China.
  • Ian Barr
    WHO Collaborating Centre for Reference and Research on Influenza, Melbourne, Victoria, Australia.
  • Leo L M Poon
    School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
  • Ahmed Abdul Quadeer
    Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
  • Matthew R McKay
    Department of Microbiology and Immunology, University of Melbourne, at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia. matthew.mckay@unimelb.edu.au.

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