Model accurately forecasts peak flu season

Model accurately forecasts peak flu season
Model accurately forecasts peak flu season

HealthDay News --  It may be possible to predict influenza outbreaks using similar techniques to how meteorologists predict the weather, study findings suggest.

Combining real-time data from Google Flu Trends, a web-based tool that uses terms people put into the search engine to figure out where influenza is occurring, with a computer assimilation model of how influenza spreads, Jeffrey Shaman, PhD, from Columbia University in New York City, and Alicia Karspeck, PhD, from the National Center for Atmospheric Research in Boulder, Colo., came up with a system to generate local forecasts for the severity and length of a particular flu outbreak.

Shaman and Karspeck then tested their findings against actual New York City influenza rates from 2003 to 2008 and found the forecasting system was able to detect peaks of influenza season seven weeks in advance from when they actually occurred. The study results were published online in Proceedings of the National Academy of Sciences.

If a relatively accurate influenza forecasting system were successfully implemented, public health officials may be able to use this information to target vaccines and medications in areas where they're needed the most, and could aid individuals' decisions on whether or not to get the annual immunization.

"This work represents an initial step in the development of a statistically rigorous system for real-time forecast of seasonal influenza," the researchers wrote.


Video: Catching Up with the Flu


References

  1. Shaman J, Karspeck A. PNAS. 2012; doi:10.1073/pnas.1208772109.
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