Scientists at the National Institutes of Health said yesterday they have come up with a computer model they say can predict infection rates of the influenza virus, and it could help people gird up for flu season in the future.
It has been well established in the scientific community that the extent to which a flu virus spreads in a given year is related to how much it has mutated from previous seasons. But these scientists set out to translate that knowledge into something more concrete.
The group looked specifically at data on virus genetics and infection rates from 16 flu seasons, from the 2008/2009 season dating back to 1993. They found that the most important factor, by far, in determining how widespread a specific flu virus became was how much the protein hemagglutinin had mutated from one year to the next. Other factors–such as weather, school schedules, and other pathogens spreading at the same time–do play a role in the severity of a flu epidemic, but only very slightly, according to the report, which appears online in the Public Library of Science’s Currents: Influenza publication.
By looking at how much a virus’ hemagglutinin gene had mutated from previous years, the scientists were able to accurately predict how many people were likely to get sick from that virus that year.
The team hopes this new model will help inform how flu vaccines are created each year. For example, scientists could analyze the genetic mutations of the various flu viruses circulating in a given year and make a more informed decision about what vaccine to release to the public, based on whether one strain shows an especially high likelihood of widespread infection.
