Abstract
BackgroundGoogle Flu Trends (GFT) uses anonymized, aggregated internet search activity to provide near-real time estimates of influenza activity. GFT estimates have shown a strong correlation with official influenza surveillance data. The 2009 influenza virus A (H1N1) pandemic [pH1N1] provided the first opportunity to evaluate GFT during a non-seasonal influenza outbreak. In September 2009, an updated United States GFT model was developed using data from the beginning of pH1N1.Methodology/Principal FindingsWe evaluated the accuracy of each U.S. GFT model by comparing weekly estimates of ILI (influenza-like illness) activity with the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). For each GFT model we calculated the correlation and RMSE (root mean square error) between model estimates and ILINet for four time periods: pre-H1N1, Summer H1N1, Winter H1N1, and H1N1 overall (Mar 2009–Dec 2009). We also compared the number of queries, query volume, and types of queries (e.g., influenza symptoms, influenza complications) in each model. Both models' estimates were highly correlated with ILINet pre-H1N1 and over the entire surveillance period, although the original model underestimated the magnitude of ILI activity during pH1N1. The updated model was more correlated with ILINet than the original model during Summer H1N1 (r = 0.95 and 0.29, respectively). The updated model included more search query terms than the original model, with more queries directly related to influenza infection, whereas the original model contained more queries related to influenza complications.ConclusionsInternet search behavior changed during pH1N1, particularly in the categories “influenza complications” and “term for influenza.” The complications associated with pH1N1, the fact that pH1N1 began in the summer rather than winter, and changes in health-seeking behavior each may have played a part. Both GFT models performed well prior to and during pH1N1, although the updated model performed better during pH1N1, especially during the summer months.
Highlights
In November, 2008, Google launched Google Flu Trends (GFT), an internet-based surveillance tool that uses aggregated Google search data to estimate influenza activity in near-real time [1]
Internet search behavior changed during pH1N1, in the categories ‘‘influenza complications’’ and ‘‘term for influenza.’’ The complications associated with pH1N1, the fact that pH1N1 began in the summer rather than winter, and changes in health-seeking behavior each may have played a part
In the United States, GFT models outpatient influenza-like illness (ILI) using publicly available ILI surveillance data provided by the U.S Centers for Disease Control and Prevention (CDC)
Summary
In November, 2008, Google launched Google Flu Trends (GFT), an internet-based surveillance tool that uses aggregated Google search data to estimate influenza activity in near-real time [1]. To account for evolving online search behavior for health information, GFT models are updated annually using the most recent official surveillance data, where available, and to utilize any newly developed modeling techniques. In the United States, GFT models outpatient influenza-like illness (ILI) using publicly available ILI surveillance data provided by the U.S Centers for Disease Control and Prevention (CDC). Google Flu Trends (GFT) uses anonymized, aggregated internet search activity to provide near-real time estimates of influenza activity. GFT estimates have shown a strong correlation with official influenza surveillance data. In September 2009, an updated United States GFT model was developed using data from the beginning of pH1N1
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