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Reassessing Google Flu Trends Data for Detection of Seasonal and Pandemic Influenza: A Comparative Epidemiological Study at Three Geographic Scales
Open accessAbstract
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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“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]
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Metadata
- 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.
- 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
- 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
- gold
- doi.org
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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