Searching for better flu surveillance? A brief communication arising from Ginsberg et al. Nature 457, 1012-1014 (2009)

Olson, Donald B.; Baer, Atar; Coletta, Michael; Deyneka, Lana; Gentry, Ryan; Ising, Amy; Murray, Erin; Paladini, Marc; Pendarvis, Justin; Soetebier, Karl; Konty, Kevin; Schulmann, Jill; Engel, Jeffrey A.; Gunn, Julia; Rolfs, Robert T.; Mostashari, Farzad

doi:10.1038/npre.2009.3493

Cited by 1 publication

(4 citation statements)

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“…Further, the updated GFT model that has been used prospectively failed to accurately capture the autumn 2009 pandemic wave in New York City, presenting the outbreak three weeks before it actually occurred. This assessment echoes earlier concerns regarding the timeliness and accuracy of internet search data for public health monitoring at the local level [17] and during the early wave of the 2009 pandemic [18]. To have missed the early wave of the 2009 pandemic is a serious shortcoming of a surveillance system – as these are times when accurate data are most critically needed for pandemic preparedness and response purposes.…”

Section: Discussionsupporting

confidence: 58%

“…Following Google's development of GFT in 2008, and the considerable excitement generated by the original publication and release of the online tool [12], [13], [16], concerns were raised regarding the tenuous relationship between internet searches and the presentation of illness to clinical or emergency medical providers [17]. We used clinical ILI surveillance data at local, regional and national scales as a proposed “ground truth” to test the ability of GFT to perform as a timely and accurate surveillance system in the US.…”

Section: Discussionmentioning

confidence: 99%

“…Concerns were raised early-on that the data-mining nature of GFT might over-fit the historical data and introduce bias in prospective use [17]. After the original GFT model missed the spring 2009 pandemic wave – an outbreak with different timing and characteristics than the outbreaks present in the retrospective model-fitting period – the GFT algorithm was modified, potentially addressing the possible over-fitting issue.…”

Section: Discussionmentioning

confidence: 99%

“…In parallel, novel approaches based on influenza-related internet search queries have been reported to yield faster detection and estimation of the intensity of influenza epidemics [10]–[16]. The public health utility of such systems for prospective monitoring and forecasting of influenza activity, however, remains unclear [17]–[21], particularly as occurred during the 2009 pandemic and the 2012/2013 epidemic season [22]–[24].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2013

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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.

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Section: Discussionsupporting

confidence: 58%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%