We also performed a secondary analysis to determine the sensitivity of the primary analysis to high-leverage, outlier observations. First, we performed simple linear regression to evaluate the association between either Google Flu Trends or CDC ILI Surveillance rates with reference viral surveillance data as standard rates. The effect of outlier observations was assessed with differences in the beta statistic (DFBETA). Individual observations were considered influential if they had a DFBETA greater than the absolute value of 2 divided by the square root of the total number of observations in the model [17] . Subsequently, all influential observations were excluded and correlation coefficients were recalculated as had been done in the primary analysis. Correlation coefficients from the sensitivity analysis were compared to the same statistic from the primary analysis to determine whether any relevant changes in the strength of correlation had occurred with the removal of influential observations. Last, Spearman Rank correlation coefficients were employed for the primary analyses and noted to yield similar results.