Reassessing Google Flu Trends Data for Detection of Seasonal and Pandemic Influenza: A Comparative Epidemiological Study at Three Geographic Scales

By D. Olson, K. Konty, M. Paladini, Cécile Viboud, L. Simonsen. PLoS Comput. Biol., 2013

319 citations recorded by the source corpus.

The goal of influenza-like illness (ILI) surveillance is to determine the timing, location and magnitude of outbreaks by monitoring the frequency and progression of clinical case incidence. Advances in computational and information technology have allowed for automated collection of higher volumes of electronic data and more timely analyses than previously possible. Novel surveillance systems, including those based on internet search query data like Google Flu Trends (GFT), are being used as surrogates for clinically-based reporting of influenza-like-illness (ILI). We investigated the reliability of GFT during the last decade (2003 to 2013), and compared weekly public health surveillance with search query data to characterize the timing and intensity of seasonal and pandemic influenza at the national (United States), regional (Mid-Atlantic) and local (New York City) levels. We identified substantial flaws in the original and updated GFT models at all three geographic scales, including completely missing the first wave of the 2009 influenza A/H1N1 pandemic, and greatly overestimating the intensity of the A/H3N2 epidemic during the 2012/2013 season. These results were obtained for both the original (2008) and the updated (2009) GFT algorithms. The performance of both models was problematic, perhaps because of changes in internet search behavior and differences in the seasonality, geographical heterogeneity and age-distribution of the epidemics between the periods of GFT model-fitting and prospective use. We conclude that GFT data may not provide reliable surveillance for seasonal or pandemic influenza and should be interpreted with caution until the algorithm can be improved and evaluated. Current internet search query data are no substitute for timely local clinical and laboratory surveillance, or national surveillance based on local data collection. New generation surveillance systems such as GFT should incorporate the use of near-real time electronic health data and computational methods for continued model-fitting and ongoing evaluation and improvement.

Subfields: Epidemiology, Physiology

View Paper: https://www.semanticscholar.org/paper/fbe3543f7d6dd4cfa1c229e64b1a7d60db4e0a8c

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In Epidemiology

Citations in the corpus

Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance (2015, background): “While Google and external researchers have worked to update and reevaluate the methodology behind GFT [19, 20, 21, 22, 23, 24], alternative and independent methods to estimate ILI in real-time are still needed.”
Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case (2020, background): “While initially regarded as a great success, GFT suffered from over-estimation of peak ILI magnitude in subsequent years (Olson et al., 2013; Lazer et al., 2014).”
Statistical physics of vaccination (2016, methodology): “However, the tool was not able, despite being updated, to predict the unfolding of the pandemic in 2009 [751, 752].”
Can we trust Big Data? Applying philosophy of science to software (2016, methodology): “Lazer et al. (2014) suggest that another possible cause for the errors in GFT is what they call algorithm dynamics undergone by Google’s search algorithm.”
Ten years of research change using Google Trends: From the perspective of big data utilizations and applications (2017, background): “A series of studies began to point to these limitations (Olson et al., 2013), and Lazer et al.”