Poster Presentation 15th Lorne Infection and Immunity 2025

Seasonal antigenic prediction of influenza A H3N2 using machine learning (#353)

Awais Wahab 1 , Daniel Palomar 2 , Ian Barr 3 4 , Leo Poon 5 6 , Ahmed Abdul Quadeer 1 , Matthew McKay 1 4
  1. Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC, Australia
  2. Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong SAR, China
  3. WHO Collaborating Centre for Reference and Research on Influenza, at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
  4. Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Parkville, VIC, Australia
  5. School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
  6. Centre for Immunology & Infection, Hong Kong Science and Technology Park, Hong Kong SAR, China

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.