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Clin Infect Dis. 2017 Jan 1;64(1):34-41. Epub 2016 Sep 26.

Evaluating Google Flu Trends in Latin America: Important Lessons for the Next Phase of Digital Disease Detection.

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Department of Epidemiology & Biostatistics, University of California at San Francisco.
Marie Bashir Institute for Infectious Diseases & Biosecurity, University of Sydney.
Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia.
Department of Virology & Emerging Infectious Diseases, US Naval Medical Research Unit No 6, Callao, Peru.
Centre for Infectious Diseases and Microbiology Laboratory Services, Level 3 Institute of Clinical Pathology and Medical Research, Pathology West, Westmead Hospital, New South Wales, Australia.
Fogarty International Center, National Institutes of Health, Bethesda, Maryland.



 Latin America has a substantial burden of influenza and rising Internet access and could benefit from real-time influenza epidemic prediction web tools such as Google Flu Trends (GFT) to assist in risk communication and resource allocation during epidemics. However, there has never been a published assessment of GFT's accuracy in most Latin American countries or in any low- to middle-income country. Our aim was to evaluate GFT in Argentina, Bolivia, Brazil, Chile, Mexico, Paraguay, Peru, and Uruguay.


 Weekly influenza-test positive proportions for the eight countries were obtained from FluNet for the period January 2011-December 2014. Concurrent weekly Google-predicted influenza activity in the same countries was abstracted from GFT. Pearson correlation coefficients between observed and Google-predicted influenza activity trends were determined for each country. Permutation tests were used to examine background seasonal correlation between FluNet and GFT by country.


 There were frequent GFT prediction errors, with correlation ranging from r = -0.53 to 0.91. GFT-predicted influenza activity best correlated with FluNet data in Mexico follow by Uruguay, Argentina, Chile, Brazil, Peru, Bolivia and Paraguay. Correlation was generally highest in the more temperate countries with more regular influenza seasonality and lowest in tropical regions. A substantial amount of autocorrelation was noted, suggestive that GFT is not fully specific for influenza virus activity.


 We note substantial inaccuracies with GFT-predicted influenza activity compared with FluNet throughout Latin America, particularly among tropical countries with irregular influenza seasonality. Our findings offer valuable lessons for future Internet-based biosurveillance tools.


Google Flu Trends; Latin America; digital epidemiology

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