Abstract
BackgroundGoogle Flu Trends was developed to estimate US influenza-like illness (ILI) rates from internet searches; however ILI does not necessarily correlate with actual influenza virus infections.Methods and FindingsInfluenza activity data from 2003–04 through 2007–08 were obtained from three US surveillance systems: Google Flu Trends, CDC Outpatient ILI Surveillance Network (CDC ILI Surveillance), and US Influenza Virologic Surveillance System (CDC Virus Surveillance). Pearson's correlation coefficients with 95% confidence intervals (95% CI) were calculated to compare surveillance data. An analysis was performed to investigate outlier observations and determine the extent to which they affected the correlations between surveillance data. Pearson's correlation coefficient describing Google Flu Trends and CDC Virus Surveillance over the study period was 0.72 (95% CI: 0.64, 0.79). The correlation between CDC ILI Surveillance and CDC Virus Surveillance over the same period was 0.85 (95% CI: 0.81, 0.89). Most of the outlier observations in both comparisons were from the 2003–04 influenza season. Exclusion of the outlier observations did not substantially improve the correlation between Google Flu Trends and CDC Virus Surveillance (0.82; 95% CI: 0.76, 0.87) or CDC ILI Surveillance and CDC Virus Surveillance (0.86; 95%CI: 0.82, 0.90).ConclusionsThis analysis demonstrates that while Google Flu Trends is highly correlated with rates of ILI, it has a lower correlation with surveillance for laboratory-confirmed influenza. Most of the outlier observations occurred during the 2003–04 influenza season that was characterized by early and intense influenza activity, which potentially altered health care seeking behavior, physician testing practices, and internet search behavior.
Highlights
The emergence of 2009 pandemic influenza A (H1N1) virus in the United States and Mexico, and its subsequent rapid global spread has underscored the importance of influenza surveillance for public health decision making [1]
This analysis demonstrates that while Google Flu Trends is highly correlated with rates of influenza-like illness (ILI), it has a lower correlation with surveillance for laboratory-confirmed influenza
Our analyses used 166 weeks of data from the 2003–04 through the 2007–08 influenza seasons obtained from three influenza surveillance systems used to monitor national and regional influenza trends
Summary
The emergence of 2009 pandemic influenza A (H1N1) virus in the United States and Mexico, and its subsequent rapid global spread has underscored the importance of influenza surveillance for public health decision making [1]. Google.org developed Google Flu Trends, a model to estimate US influenza-like illness (ILI) rates from internet searches. During the 2007–08 influenza season, Google Flu Trends estimates were highly correlated to CDC surveillance for ILI, with a mean correlation coefficient over nine US Census Regions of 0.97 [2]. For the purpose of CDC influenza surveillance, ILI is defined as a fever $37.8 ̊C and a cough and/or a sore throat without known etiology In the United States, during the spring wave of the 2009 H1N1 outbreak from March through August 2009, the proportion of positive influenza laboratory tests did not exceed 45% (http://www.cdc.gov/flu/ weekly, accessed 09/04/09). Google Flu Trends was developed to estimate US influenza-like illness (ILI) rates from internet searches; ILI does not necessarily correlate with actual influenza virus infections
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