AI-powered tool predicts the impact of influenza virus mutations
Influenza, known for its ability to mutate, presents an ongoing challenge to public health. These mutations can influence how well the immune system can recognise and respond to the virus, and impact the effectiveness of influenza vaccines.
The ongoing tracking of these changes and updating vaccines accordingly are a major challenge.
The World Health Organization (WHO) Global Influenza Surveillance and Response System (GISRS) system relies on genetic sequencing and extensive laboratory tests, conducted by National Influenza Centres and Influenza Collaborating Centres worldwide, to assess circulating influenza viruses.
These test results inform decisions on vaccine updates for the upcoming season. However, this process is resource-intensive, time-consuming and requires extensive coordination among laboratories across the globe.
To address these challenges, a team of researchers, led by University of Melbourne ARC Future Fellow Professor Matthew McKay, from the Faculty of Engineering and Information Technology's Department of Electrical and Electronic Engineering, and Lab Head at the Doherty Institute, has developed a machine learning model that can accurately predict antigenic changes in circulating influenza viruses and their capacity to evade immunity from prior infections or vaccinations. Antigenic changes refer to changes in the virus that enable it to evade immunity from prior infections or vaccinations.
The research, published in 'Nature Communications', was showcased by the journal as one of the 50 best papers recently published in the category of 'Microbiology and Infectious Diseases'.
The model uses genetic sequence data of the viral strain and information from past influenza seasons to predict the outcome of antigenic lab tests that are crucial for determining how a viral strain will react to the current influenza vaccine, whether a vaccine update is required, and if so, what the best vaccine virus would be.
Dr Ahmed Abdul Quadeer, a Senior Research Fellow in the Department of Electrical and Electronic Engineering and co-leader of the research, highlighted the model’s sophistication in predicting viral strain changes.
“The mapping between genetic and antigenic changes in influenza virus is complex,” Dr Quadeer said.
Our data-driven machine learning model does a very good job in learning this linkage and provides accurate predictions of the virus's antigenic properties, as we found across the 14 seasons we tested the model on.
Presently, only a portion of circulating influenza viruses is tested every season, due to practical constraints, such as resources, cost and time. Thanks to its capacity to swiftly analyse vast amounts of data, the developed machine learning model has the potential to predict antigenic properties for all sequenced circulating viruses in that season.
By providing a more complete understanding of the influenza antigenic landscape, the data-driven model can complement existing surveillance protocols, guide vaccine strain selection and offer valuable insights into virus evolution.
The Royal Melbourne Hospital’s Professor Ian Barr, Deputy Director of the WHO Collaborating Centre for Reference and Research on Influenza at the Doherty Institute, noted the transformative potential of the machine learning model.
“Machine learning and AI tools present scientific advances that have the potential to transform our approach to managing seasonal influenza and potentially other infectious diseases,” Professor Barr said.
Researchers have packaged the model for seasonal influenza antigenic prediction into an easy-to-use web application.
The research has been funded by the Australian Research Council and Hong Kong Research Grants Council. Combining genetic sequence data analysis, machine learning, influenza virology and immunology, it involved collaboration from the University of Melbourne, the Doherty Institute (including the WHO Collaborating Centre for Research and Reference on Influenza), the Hong Kong University of Science and Technology and The University of Hong Kong.