David Lazer, Ryan Kennedy, Gary King, and Alessandro Vespignani. 2014. “The Parable of Google Flu: Traps in Big Data Analysis.” Science, 343, 14 March, Pp. 1203-1205. Publisher's Version Copy at https://tinyurl.com/y8q5wmk5
Abstract: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?
See also "Google Flu Trends Still Appears Sick: An Evaluation of the 2013‐2014 Flu Season"