Novel tool forecasts severity of coming influenza season

A novel tool accurately forecasted details of the 2016 to 2017 influenza season based on the epidemiology and evolution of the virus before the season began, according to findings recently published in Science Translational Medicine.

 “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,” Mercedes Pascual, PhD, professor of ecology and evolution at the University of Chicago, said in a press release accompanying the study. “You could imagine using our model to make an early prediction about overall severity of the season, and then use other methods to forecast the timing of the outbreak once it begins.”

The researchers’ new model combined two current methods — one that tracks genetic changes to different strains of the influenza virus to predict the coming year’s influenza lineage and another that models the spread of illness — to complement both existing tools. Although most epidemiological models can be used only after the annual influenza outbreak has already begun, Pascual and colleagues’ tool is feasible for use in predicting details of outbreaks before they begin, the researchers reported.

The model accurately predicted the cumulative incidence of influenza in the United States for each season from 2002 to 2016, Pascual and colleagues reported, covering specific subtypes such as H3N2, pandemic and seasonal H1N1 and type B. It also accurately predicted that the upcoming 2016 to 2017 influenza season would include a higher incidence of H3N2.

“We have tested the approach as far as possible with the currently available data for the aggregated U.S.,” Pascual said in an interview transcript released with the study. “In terms of adoption, as for any forecasting method, we need to test it further going forward; for example, we have produced a prediction for H3N2 influenza this coming season. We now have to wait and see how well we do.” – by Andy Polhamus

Disclosures: The authors report no relevant financial disclosures.