Seasonal antigenic prediction of influenza A H3N2 using machine learning

Syed Awais W. Shah, Daniel P. Palomar, Ian Barr, Leo L. M. Poon, Ahmed Abdul Quadeer () and Matthew R. McKay ()
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Syed Awais W. Shah: Clear Water Bay
Daniel P. Palomar: Clear Water Bay
Ian Barr: WHO Collaborating Centre for Reference and Research on Influenza
Leo L. M. Poon: The University of Hong Kong
Ahmed Abdul Quadeer: Clear Water Bay
Matthew R. McKay: at The Peter Doherty Institute for Infection and Immunity

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-47862-9

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