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Cited by 320Data-Driven Disease SurveillanceInfluenza Virus Research StudiesPharmacovigilance and Adverse Drug Reactions

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

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Olson, Donald R., Konty, Kevin J., Paladini, Marc, Viboud, Cecile, Simonsen, Lone·2013·PLoS Computational Biology
DOI 10.1371/journal.pcbi.1003256PMC PMC3798275
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Abstract

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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  • The ad hoc method of throwing out peculiar search terms failed when GFT completely missed the nonseasonal 2009 influenza A-H1N1 pandemic.

    “This ad hoc method of throwing out peculiar search terms failed when GFT completely missed the nonseasonal 2009 infl uenza A–H1N1 pandemic ( 2, 14).”

    From The Parable of Google Flu: Traps in Big Data Analysis (2014) · cites this paper as [2]

    The Discussion (page unavailable) says the original GFT model missed the spring 2009 pandemic wave, supporting the claim about its failure to detect the nonseasonal outbreak. It does not say GFT completely missed the entire 2009 pandemic. The Introduction describes automated selection of the original model’s search terms, not an ad hoc method of throwing out peculiar terms. (LLM, conf=0.86)

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Research works printed but not linked: 3

  1. [17]Olson DR, Baer A, Coletta MA, Deyneka L, Gentry R, et al (2009) Searching for better flu surveillance? A brief communication arising. Nature Precedings 3493 Available: http://precedings.nature.com/documents/3493/version/1.
  2. [18]Karon AE, Heffernan RT (2009) Google Flu Trends versus ED Syndromic Surveillance, Wisconsin 2009. Annual Conference of the International Society for Disease Surveillance (ISDS), Miami, FL, December 2009.
  3. [34]Simonsen L, Olson DR, Viboud C, Heiman E, Taylor R, et al.. (2005) Pandemic Influenza and Mortality: Past Evidence and Projections for the Future. In The Threat of Pandemic Influenza: Are We Ready? Board on Global Health, Institute of Medicine, National Academies Press, DC, 2005, pp 89–106.

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Authors
Olson, D. R., Konty, K. J., Paladini, M., Viboud, C., & Simonsen, L.
Affiliations
  • New York City Department of Health and Mental Hygiene (US)
  • National Institutes of Health (US)
Corresponding author
Olson, Donald R.
ORCID
Venue
PLoS Computational Biology
Publisher
Public Library of Science (PLoS)
Published
2013-10-17
Volume
9
Issue
10
Pages
e1003256
ISSN
  • 1553-734X (ISSN-L)
  • 1553-7358
DOI
10.1371/journal.pcbi.1003256
PMC
PMC3798275
OpenAlex
W2080321187
Cited by
320
References
51
Citations by year
2026: 9 · 2025: 9 · 2024: 5 · 2023: 9 · 2022: 14 · 2021: 32 · 2020: 38 · 2019: 28 · 2018: 32 · 2017: 39 · 2016: 40 · 2015: 31 · 2014: 32 · 2013: 2
FWCI
25.55
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS Computational Biology (publishedVersion, public-domain)
  • Europe PMC (PubMed Central) (publishedVersion, public-domain)
  • PubMed Central (publishedVersion, public-domain)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
Funders
  • U.S. Department of Homeland Security
  • Alfred P. Sloan Foundation (2010-12-14)
  • New York City Department of Health and Mental Hygiene
  • Science and Technology Directorate
  • Fogarty International Center
Topics
  • Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
  • Health Sciences › Medicine › Epidemiology › Influenza Virus Research Studies
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
Keywords
Google Flu Trends, influenza surveillance, influenza-like illness, influenza A (H1N1) pandemic, seasonal influenza, H3N2, influenza seasonality, electronic health data, New York City, Mid-Atlantic region
MeSH terms
Algorithms, Computer Simulation, Humans, Influenza, Human, Population Surveillance, Seasons, United States, Sentinel Surveillance, Computational Biology, Search Engine, Pandemics

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