Google Flu Trends may have overestimated this season's ailments

"Just Google it" might not have been the best thing to say this past flu season, as a glitch in Google Flu Trends may have caused a drastic overestimation of peak flu levels, Nature reports. The glitch is a "temporary setback for a promising strategy," according to the article, with Google sure to refine its algorithms.

"It is hard to think today that one can provide disease surveillance without existing systems," Alain-Jacques Valleron, an epidemiologist at the Pierre and Marie Curie University in Paris, told Nature. "The new systems depend too much on old existing ones to be able to live without them."

Last year's flu season, starting in November and peaking after Christmas, made it the earliest flu season since 2003, and caused more serious illness and deaths than usual, with H3N2, the most virulent of flu strains, as this year's predominant strain.

Google Flu Trends was thought to be a good "baseline indicator" of epidemic trends at the beginning of the season, but could not replace traditional methods, despite its quick feedback system. The system has performed well in past flu seasons, estimating at times to exactly match the Center for Disease Control's own surveillance data. This past season, however, its estimate for the Christmas time national peak of flu was almost double the CDC's, with state date showing larger discrepancies.

Google refused to comment to Nature about this year's flu season difficulties. Some researchers suggested that it may have been the fault of widespread flu coverage, triggering flu-related searches.

"You need to be constantly adapting these models, they don't work in a vacuum," John Brownstein, an epidemiologist at Harvard Medical School, told Nature. "You need to recalibrate them every year."

Researchers at Johns Hopkins University used Twitter to produce real-time results of flu data this flu season, tracking tweets like "I have the flu" from ones that may have read "I'm worried about getting the flu" to get numbers of those infected.

To learn more:
- read the Nature report

Related Articles:
#Flu: Researchers analyze Twitter timelines to track outbreaks in real time
Google Flu Trends a good warning system for EDs
CDC turns to social, mobile media for swine flu updates

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