When Google got flu wrong

By D. Butler. Nature, 2013

496 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7f24edace5e9f5588422cbd6ba3d57dfcf87cbdb

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In Epidemiology

Citations in the corpus

Beta regression for time series analysis of bounded data, with application to Canada Google Flu Trends. (2014, background): “indicate that peak influenza levels in winter 2012–2013 have been overestimated, as a consequence of an increased number of search queries related to influenza strains which caused more serious illness and deaths than usual [Butler (2013)].”
Accurate estimation of influenza epidemics using Google search data via ARGO (2015, unknown): “However, significant discrepancies between GFT’s flu estimates and those measured by the Centers for Disease Control (CDC) in subsequent years led to considerable doubt about the value of digital disease detection systems [13].”
Online and Social Media Data As an Imperfect Continuous Panel Survey (2016, background): “That said, recent work [67] shows that that relationship failed soon after the initial papers were published — correlating topline results with outcomes is fragile when the underlying input data is in constant flux.”
The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts (2015, background): “6 However, the efficacy of such platforms remains questionable (Butler 2013).”
Surveillance, Snowden, and Big Data: Capacities, consequences, critique (2014, background): “As it happens, the conventional forms of analysis still seem to have a high degree of validity compared with crowd-sourced methods (Butler, 2013).”
The challenges and limits of big data algorithms in technocratic governance (2016, background): “The latter is illustrated by the Google flu prediction resulting in wrong estimates which might be caused by differences in media coverage (Butler, 2013).”
Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance (2015, methodology): “ously provides real-time estimates of ILI. Even though GFT was initially hailed as a success, its inaccuracies in multiple time periods of high ILI have lead to doubts about the utility of these data [18]. While Google and external researchers have worked to update and reevaluate the methodology beh”