SFU Research: Flu Strain Evolution May Inform Vaccine Development

source :

Simon Fraser University researchers studying the evolutionary history of flu viruses have found that a new quantitative analysis of how they evolved could help predict future strains. The research is based on a field known as phylogenetics, which focuses on how groups of organisms are evolutionarily related, and was published in the journal Science Advances.

Researchers used large phylogenetic ‘trees’ to predict which strains are most likely to grow during the upcoming flu season, and found that this approach was quite effective at detecting future strains of the influenza virus, and could be another tool in the toolbox to guide seasonality. development of flu vaccines.

“The COVID-19 pandemic has caused a significant change in the dynamics of influenza transmission,” said Caroline Colijn, professor of mathematics and Canada Research Chair. “We investigated how machine learning can identify influenza virus sequences that are potentially good candidates for inclusion in seasonal flu vaccines.”

For vaccination to be successful, the specific viruses in seasonal flu vaccines must be comparable to the flu viruses that will circulate in the coming season, Colijn explains. The effectiveness of seasonal flu vaccines varies (for example, from 25 to 75 percent in children) and depends on whether the strains circulating match those projected and included in the vaccine.

Researchers studied phylogenetic trees, essentially the family tree of the influenza virus, with information from the Global Initiative on Sharing Avian Influenza Data (GISAID). After creating phylogenies using more than 65,000 RNA sequences of influenza surface proteins collected between 1970 and 2020, they used features in these trees to identify strains likely to increase in numbers in the coming season.

Seasonal flu vaccine is designed to protect against common flu viruses, including H3N2, H1N1, and B. Their research specifically focused on the H3N2 subtype of the flu virus.

“We were able to identify candidate strains similar to those proposed by the World Health Organization, indicating that this machine learning approach could help select vaccine strains,” says Colijn.


CAROLINE COLIJN, Canada 150 Research Chair in Mathematics for Evolution, Infection and Public Health

source :

Related Articles

Leave a Reply

Your email address will not be published. Required fields are marked *

Back to top button