Editors,

The recent paper on Using Google Trends (GT) to Estimate the Incidence of Influenza-Like Illness in Argentina ¹1 Orellano PW, Reynoso JI, Antman J, Argibay O. Uso de la herramienta Google Trends para estimar la incidencia de enfermedades tipo influenza en Argentina. Cad Saúde Pública 2015; 31:691-700. is very interesting. Orellano et al. studied Google Flu Trends (GFT) and GT with a conclusion regarding “the utility of GT to complement influenza surveillance”. Indeed, the usefulness of GFT and GT has been mentioned in some earlier reports ²2 Araz OM, Bentley D, Muelleman RL. Using Google Flu Trends data in forecasting influenza-like-illness related ED visits in Omaha, Nebraska. Am J Emerg Med 2014; 32:1016-23.^,33 Malik MT, Gumel A, Thompson LH, Strome T, Mahmud SM. “Google flu trends” and emergency department triage data predicted the 2009 pandemic H1N1 waves in Manitoba. Can J Public Health 2011; 102:294-7.. However, as a computational model, there are several things to be considered in the simulation ⁴4 Wiwanitkit V. Google Flu for forecasting influenza-like illness. Am J Emerg Med 2014; 32:1417.. Under- or over-estimation can be expected and this is still the present problem in using the Google system for predicting influenza ⁴4 Wiwanitkit V. Google Flu for forecasting influenza-like illness. Am J Emerg Med 2014; 32:1417.. There is a need for modifications of GT and GFT into a more specific tool that is appropriate for each context. A good example of this is the development of FluBreaks by Pervaiz et al. ⁵5 Pervaiz F, Pervaiz M, Abdur Rehman N, Saif U. FluBreaks: early epidemic detection from Google flu trends. J Med Internet Res 2012; 14:e125..

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