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Google Flu Trends Still Appears Sick: An Evaluation of the 2013?2014 Flu Season

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Listed:
  • Lazer, David
  • Ryan Kennedy
  • Gary King
  • Alessandro Vespignani

Abstract

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.

Suggested Citation

  • Lazer, David & Ryan Kennedy & Gary King & Alessandro Vespignani, 2014. "Google Flu Trends Still Appears Sick: An Evaluation of the 2013?2014 Flu Season," Working Paper 155056, Harvard University OpenScholar.
    • Handle: RePEc:qsh:wpaper:155056
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    File URL: http://gking.harvard.edu//node/155056
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    Cited by:

    1. Pichler, Stefan & Ziebarth, Nicolas R., 2017. "The pros and cons of sick pay schemes: Testing for contagious presenteeism and noncontagious absenteeism behavior," Journal of Public Economics, Elsevier, vol. 156(C), pages 14-33.
    2. Stefan Pichler & Nicolas R. Ziebarth, 2015. "The Pros and Cons of Sick Pay Schemes: Testing for Contagious Presenteeism and Shirking Behavior," Discussion Papers of DIW Berlin 1509, DIW Berlin, German Institute for Economic Research.
    3. Yulin Hswen & Elad Yom-Tov, 2021. "Analysis of a Vaping-Associated Lung Injury Outbreak through Participatory Surveillance and Archival Internet Data," IJERPH, MDPI, vol. 18(15), pages 1-17, August.
    4. Gerlovina Inna & van der Laan Mark J. & Hubbard Alan, 2017. "Big Data, Small Sample: Edgeworth Expansions Provide a Cautionary Tale," The International Journal of Biostatistics, De Gruyter, vol. 13(1), pages 1-6, May.
    5. Pichler, Stefan & Ziebarth, Nicolas R., 2019. "Reprint of: The pros and cons of sick pay schemes: Testing for contagious presenteeism and noncontagious absenteeism behavior," Journal of Public Economics, Elsevier, vol. 171(C), pages 86-104.

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