title: Improving Google Flu Trends estimates for the United States through transformation.
abstract: Google Flu Trends (GFT) uses Internet search queries in an effort to provide early warning of increases in influenza-like illness (ILI). In the United States, GFT estimates the percentage of physician visits related to ILI (%ILINet) reported by the Centers for Disease Control and Prevention (CDC). However, during the 2012-13 influenza season, GFT overestimated %ILINet by an appreciable amount and estimated the peak in incidence three weeks late. Using data from 2010-14, we investigated the relationship between GFT estimates (%GFT) and %ILINet. Based on the relationship between the relative change in %GFT and the relative change in %ILINet, we transformed %GFT estimates to better correspond with %ILINet values. In 2010-13, our transformed %GFT estimates were within ± 10% of %ILINet values for 17 of the 29 weeks that %ILINet was above the seasonal baseline value determined by the CDC; in contrast, the original %GFT estimates were within ± 10% of %ILINet values for only two of these 29 weeks. Relative to the %ILINet peak in 2012-13, the peak in our transformed %GFT estimates was 2% lower and one week later, whereas the peak in the original %GFT estimates was 74% higher and three weeks later. The same transformation improved %GFT estimates using the recalibrated 2013 GFT model in early 2013-14. Our transformed %GFT estimates can be calculated approximately one week before %ILINet values are reported by the CDC and the transformation equation was stable over the time period investigated (2010-13). We anticipate our results will facilitate future use of GFT.
authors.affiliation: School of Public Health, University of Alberta, Edmonton, Alberta, Canada.
authors.forename: Leah J
authors.initials: LJ
authors.lastname: Martin
authors.affiliation: School of Public Health, University of Alberta, Edmonton, Alberta, Canada; Zhejiang University, Hangzhou, China.
authors.forename: Biying
authors.initials: B
authors.lastname: Xu
authors.affiliation: School of Public Health, University of Alberta, Edmonton, Alberta, Canada.
authors.forename: Yutaka
authors.initials: Y
authors.lastname: Yasui
mesh_terms: D000553:Ambulatory Care / Q000706:statistics & numerical data; D002487:Centers for Disease Control and Prevention, U.S.; D057225:Data Mining; D004196:Disease Outbreaks; D062665:Epidemiological Monitoring; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology*; D020407:Internet; D012621:Seasons; D014481:United States / Q000453:epidemiology