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.
Highlights
In the United States, traditional influenza and influenza-like illness (ILI) surveillance data are available from the Centers for Disease Control and PLOS ONE | DOI:10.1371/journal.pone.0109209 December 31, 2014Improving Google Flu Trends EstimatesPrevention (CDC) and include data from ILINet, an outpatient surveillance system that measures the percentage of physician visits related to ILI (%ILINet) [1]
Given the inaccuracies and criticisms of Google Flu Trends (GFT) during the 2012–13 season, we investigated the relationship between GFT estimates and their intended target (%ILINet) and transformed GFT estimates to improve their estimation of %ILINet in the United States
We found that the relative change in %GFT closely approximates the relative change from p%ILINet to f%ILINet and this relationship can inform %GFT for the current week, i, to better match its target, f%ILINet in week i
Summary
In the United States, traditional influenza and influenza-like illness (ILI) surveillance data are available from the Centers for Disease Control and PLOS ONE | DOI:10.1371/journal.pone.0109209 December 31, 2014Improving Google Flu Trends EstimatesPrevention (CDC) and include data from ILINet, an outpatient surveillance system that measures the percentage of physician visits related to ILI (%ILINet) [1]. Ginsberg et al developed a model to estimate %ILINet using Internet search queries and to ‘‘provide one of the most timely, broad-reaching influenza monitoring systems available today’’ [2] This model, known as Google Flu Trends (GFT), produces estimates for 29 countries [3] and has been incorporated into ILI-related research [4,5,6,7,8]. During the ‘‘moderately severe’’ influenza season of 2012–13 [11], GFT overestimated the magnitude of %ILINet in the United States by an appreciable amount and estimated the peak three weeks late [9, 12]; they have since recalibrated their model for the United States for the 2013–14 season [13] These inaccuracies in 2009 and 2012–13 have created uncertainty about GFT estimates [12] and have been attributed to changes in Internet search behaviour [10, 12] as well as to changes made by Google to its search algorithm [14]. The recalibrated 2013 GFT model was not made available for more than nine months after the overestimated peak in January 2013, a delay that decreases the public health value of GFT as an early warning tool for pandemic preparedness and seasonal influenza surveillance
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