assessing google flu trends performance in the united states during the 2009 influenza virus a (h1n1) pandemic评估google流感趋势表现在美国在2009年的流感病毒甲型h1n1流感大流行.pdf

Assessing Google Flu Trends Performance in the United States during the 2009 Influenza Virus A (H1N1) Pandemic 1 2 3 1 Samantha Cook , Corrie Conrad *, Ashley L. Fowlkes , Matthew H. Mohebbi 1 Google, Inc., New York, New York, United States of America, 2 Google, Inc., London, United Kingdom, 3 Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America Abstract Background: 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. 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 Findings: We 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 mo