by Researchers from Simon Fraser University have conducted a study on the evolutionary history of flu viruses, discovering that a new quantitative analysis of their evolution may aid in predicting future strains. Published in the journal Science Advances, the research draws on the field of phylogenetics, which examines the evolutionary relationships between groups of organisms.
Using large phylogenetic “trees,” the researchers were able to predict which strains are most likely to dominate during the upcoming flu season. They found that this approach was moderately effective in detecting future strains of the influenza virus and could serve as a valuable tool in guiding the development of seasonal flu vaccines.
To ensure the success of vaccination, the specific viruses included in the seasonal flu vaccines must closely match those that will circulate in the upcoming season, explains one of the researchers, Colijn. The effectiveness of seasonal influenza vaccines often varies, ranging from 25-75% in children, depending on whether the circulating strains align with those projected and included in the vaccine.
The researchers examined phylogenetic trees, which outline the family tree of the influenza virus, using information from the Global Initiative on Sharing Avian Influenza Data (GISAID). By creating phylogenies utilizing over 65,000 RNA sequences from influenza’s surface proteins, collected between 1970 and 2020, they were able to identify features within these trees that indicated strains likely to increase in number in the upcoming season.
The study focused specifically on the H3N2 subtype of influenza virus, as the seasonal influenza vaccine is designed to protect against common strains including H3N2, H1N1, and B. The researchers found that their machine learning approach successfully identified candidate strains similar to those proposed by the World Health Organization, suggesting that it can contribute to the selection of vaccine strains.
Incorporating this quantitative analysis into vaccine development could help improve the accuracy of strain predictions, ultimately enhancing the effectiveness of seasonal flu vaccines. With further research and refinement, this approach may allow for better preparation and prevention of influenza outbreaks in the future.
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