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NEW YORK (GenomeWeb) ? By folding in genetic data on how influenza viruses are evolving, researchers can better forecast how bad a coming flu season will be.
The seasonal flu accounts for some 1 billion illnesses a year and for 250,000 to 500,000 deaths a year worldwide. Four strains ? H3N2, H1N1, and two influenza B viruses ? circulate annually, with slight genetic variations from year to year. As they reported today in Science Translational Medicine, University of Chicago researchers have now taken this genetic evolution into account, alongside other data, in a new model to predict H3N2 incidence.
"Combining information about the evolution of the virus with epidemiological data will generate disease forecasts before the season begins, significantly earlier than what is currently possible," senior author Mercedes Pascual, professor of ecology and evolution at UChicago, said in a statement.
She and her colleagues first tweaked an epidemiological model to explain monthly flu incidence data collected in the US between October 2002 and June 2016. A basic model following the susceptible-infected-recovered-susceptible formulation that divvies the population into immune, infected, and non-immune individuals could reproduce the average seasonality of H3N2 flu incidence, but not variation from year to year, the researchers reported.
The seasonal flu accounts for some 1 billion illnesses a year and for 250,000 to 500,000 deaths a year worldwide. Four strains ? H3N2, H1N1, and two influenza B viruses ? circulate annually, with slight genetic variations from year to year. As they reported today in Science Translational Medicine, University of Chicago researchers have now taken this genetic evolution into account, alongside other data, in a new model to predict H3N2 incidence.
"Combining information about the evolution of the virus with epidemiological data will generate disease forecasts before the season begins, significantly earlier than what is currently possible," senior author Mercedes Pascual, professor of ecology and evolution at UChicago, said in a statement.
She and her colleagues first tweaked an epidemiological model to explain monthly flu incidence data collected in the US between October 2002 and June 2016. A basic model following the susceptible-infected-recovered-susceptible formulation that divvies the population into immune, infected, and non-immune individuals could reproduce the average seasonality of H3N2 flu incidence, but not variation from year to year, the researchers reported.
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