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Reassessing Google Flu Trends data for detection of seasonal and pandemic influenza: a comparative epidemiological study at three geographic scales.

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.

SUBMITTER: Olson DR

PROVIDER: S-EPMC3798275 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Reassessing Google Flu Trends data for detection of seasonal and pandemic influenza: a comparative epidemiological study at three geographic scales.

Olson Donald R DR Konty Kevin J KJ Paladini Marc M Viboud Cecile C Simonsen Lone L

PLoS computational biology 20131017 10

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 sur ...[more]

PMID: 24146603

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Project description:BackgroundAssessment of the effect of influenza on populations, including risk of infection, illness if infected, illness severity, and consultation rates, is essential to inform future control and prevention. We aimed to compare the community burden and severity of seasonal and pandemic influenza across different age groups and study years and gain insight into the extent to which traditional surveillance underestimates this burden.MethodsUsing preseason and postseason serology, weekly illness reporting, and RT-PCR identification of influenza from nasal swabs, we tracked the course of seasonal and pandemic influenza over five successive cohorts (England 2006-11; 5448 person-seasons' follow-up). We compared burden and severity of seasonal and pandemic strains. We weighted analyses to the age and regional structure of England to give nationally representative estimates. We compared symptom profiles over the first week of illness for different strains of PCR-confirmed influenza and non-influenza viruses using ordinal logistic regression with symptom severity grade as the outcome variable.FindingsBased on four-fold titre rises in strain-specific serology, on average influenza infected 18% (95% CI 16-22) of unvaccinated people each winter. Of those infected there were 69 respiratory illnesses per 100 person-influenza-seasons compared with 44 per 100 in those not infected with influenza. The age-adjusted attributable rate of illness if infected was 23 illnesses per 100 person-seasons (13-34), suggesting most influenza infections are asymptomatic. 25% (18-35) of all people with serologically confirmed infections had PCR-confirmed disease. 17% (10-26) of people with PCR-confirmed influenza had medically attended illness. These figures did not differ significantly when comparing pandemic with seasonal influenza. Of PCR-confirmed cases, people infected with the 2009 pandemic strain had markedly less severe symptoms than those infected with seasonal H3N2.InterpretationSeasonal influenza and the 2009 pandemic strain were characterised by similar high rates of mainly asymptomatic infection with most symptomatic cases self-managing without medical consultation. In the community the 2009 pandemic strain caused milder symptoms than seasonal H3N2.FundingMedical Research Council and the Wellcome Trust.

Dataset Information

Reassessing Google Flu Trends data for detection of seasonal and pandemic influenza: a comparative epidemiological study at three geographic scales.

Publications

Reassessing Google Flu Trends data for detection of seasonal and pandemic influenza: a comparative epidemiological study at three geographic scales.

Similar Datasets

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