The Parable of Google Flu: Traps in Big Data Analysis

Large errors in flu prediction were largely avoidable, which offers lessons for the use of big data. In February 2013, Google Flu Trends (GFT) made headlines but not for a reason that Google executives or the creators of the flu tracking system would have hoped. Nature reported that GFT was predicti...

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Bibliographic Details
Published inScience (American Association for the Advancement of Science) Vol. 343; no. 6176; pp. 1203 - 1205
Main Authors Lazer, David, Kennedy, Ryan, King, Gary, Vespignani, Alessandro
Format Journal Article
LanguageEnglish
Published Washington American Association for the Advancement of Science 14.03.2014
The American Association for the Advancement of Science
Subjects
Algorithms
Big data
Blogs
Data collection
Epidemiology
Errors
Influenza
Online searching
Parables
POLICY FORUM
Search engines
Search terms
Shamans
Social media
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Summary:Large errors in flu prediction were largely avoidable, which offers lessons for the use of big data. In February 2013, Google Flu Trends (GFT) made headlines but not for a reason that Google executives or the creators of the flu tracking system would have hoped. Nature reported that GFT was predicting more than double the proportion of doctor visits for influenza-like illness (ILI) than the Centers for Disease Control and Prevention (CDC), which bases its estimates on surveillance reports from laboratories across the United States ( 1 , 2 ). This happened despite the fact that GFT was built to predict CDC reports. Given that GFT is often held up as an exemplary use of big data ( 3 , 4 ), what lessons can we draw from this error?
ISSN:0036-8075
1095-9203
DOI:10.1126/science.1248506