title: [Using Google Trends to estimate the incidence of influenza-like illness in Argentina].
abstract: The aim of this study was to find a model to estimate the incidence of influenza-like illness (ILI) from the Google Trends (GT) related to influenza. ILI surveillance data from 2012 through 2013 were obtained from the National Health Surveillance System, Argentina. Internet search data were downloaded from the GT search engine database using 6 influenza-related queries: flu, fever, cough, sore throat, paracetamol, and ibuprofen. A Poisson regression model was developed to compare surveillance data and internet search trends for the year 2012. The model's results were validated using surveillance data for the year 2013 and results of the Google Flu Trends (GFT) tool. ILI incidence from the surveillance system showed strong correlations with ILI estimates from the GT model (r = 0.927) and from the GFT tool (r = 0.943). However, the GFT tool overestimates (by nearly twofold) the highest ILI incidence, while the GT model underestimates the highest incidence by a factor of 0.7. These results demonstrate the utility of GT to complement influenza surveillance.
authors.forename: Pablo Wenceslao
authors.initials: PW
authors.lastname: Orellano
authors.forename: Julieta Itatí
authors.initials: JI
authors.lastname: Reynoso
authors.forename: Julián
authors.initials: J
authors.lastname: Antman
authors.forename: Osvaldo
authors.initials: O
authors.lastname: Argibay
mesh_terms: D001118:Argentina / Q000453:epidemiology; D018563:Disease Notification; D006801:Humans; D015994:Incidence; D007251:Influenza, Human / Q000453:epidemiology*; D020407:Internet / Q000706:statistics & numerical data*; D016012:Poisson Distribution; D011159:Population Surveillance; D057386:Search Engine / Q000379:methods / Q000706:statistics & numerical data*