The H9N2 subtype in the markets is of concern to humans because many isolates, including all of those sequenced in this study, possess the L226 mutation in the receptor binding pocket of the HA1 protein. This mutation confers a higher degree of specificity to sialic acid residues that contain the human-like a-2,6 linkage [39] , and it has allowed the H9N2-subtype virus to grow in human airway epithelial cells [40] and be directly transmitted in ferrets [34] . The L226 mutation and the potential for aerosol transmission increases the risk of H9N2-subtype infection for humans that work in and visit the markets. Moreover, the H9N2 subtype may be modulating the morbidity and mortality that is associated with infection with the HP H5N1 subtype. Thus, early detection of the HP H5N1 subtype requires continuing surveillance efforts in the poultry markets. Monitoring Influenza Activity in the United States: A Comparison of Traditional Surveillance Systems with Google Flu Trends BACKGROUND: Google 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 FINDINGS: Influenza 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). CONCLUSIONS: This 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. 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] . Recently, Google.org developed Google Flu Trends, a model to estimate US influenza-like illness (ILI) rates from internet searches. The model was fit to CDC sentinel provider surveillance data for ILI from 2003 to 2007 and prospectively validated using the same surveillance system during the 2007-08 influenza season. 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] .