Google Flu Trends Still Appears Sick: An Evaluation of the 2013-2014 Flu Season
Author(s) -
David Lazer,
Ryan Kennedy,
Gary King,
Alessandro Vespignani
Publication year - 2014
Publication title -
ssrn electronic journal
Language(s) - English
Resource type - Journals
ISSN - 1556-5068
DOI - 10.2139/ssrn.2408560
Subject(s) - download , flu season , world wide web , computer science , internet privacy , biology , virology , vaccination
Last year was difficult for Google Flu Trends (GFT). In early 2013, Nature reported that GFT was estimating more than double the percentage of doctor visits for influenza like illness than the Centers for Disease Control and Prevention s (CDC) sentinel reports during the 2012 2013 flu season (1). Given that GFT was designed to forecast upcoming CDC reports, this was a problematic finding. In March 2014, our report in Science found that the overestimation problem in GFT was also present in the 2011 2012 flu season (2). The report also found strong evidence of autocorrelation and seasonality in the GFT errors, and presented evidence that the issues were likely, at least in part, due to modifications made by Google s search algorithm and the decision by GFT engineers not to use previous CDC reports or seasonality estimates in their models what the article labeled algorithm dynamics and big data hubris respectively. Moreover, the report and the supporting online materials detailed how difficult/impossible it is to replicate the GFT results, undermining independent efforts to explore the source of GFT errors and formulate improvements.
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