Google Flu Trends: Correlation With Emergency Department Influenza Rates and Crowding Metrics

Dugas, Andrea; Hsieh, Yu‐Hsiang; Levin, Scott R.; Pines, Jesse M.; Mareiniss, Darren P.; Mohareb, Amir M.; Gaydos, Charlotte A.; Perl, Trish M.; Rothman, Richard E.

doi:10.1093/cid/cir883

Cited by 213 publications

(127 citation statements)

References 27 publications

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“…Correlations from three of the models were on a par with those of previous studies, and slightly lower for one model, demonstrating the feasibility of using search query surveillance for malaria [6,7,10,11,14]. Recently, other novel internet-based active surveillance methods for malaria have been explored, however search queries provide a passively available and thus continuous and potentially earlier source of information than active surveillance methods.…”

Section: Discussionmentioning

confidence: 78%

Using search queries for malaria surveillance, Thailand

Ocampo

Chunara

Brownstein

2013

Malar J

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BackgroundInternet search query trends have been shown to correlate with incidence trends for select infectious diseases and countries. Herein, the first use of Google search queries for malaria surveillance is investigated. The research focuses on Thailand where real-time malaria surveillance is crucial as malaria is re-emerging and developing resistance to pharmaceuticals in the region.MethodsOfficial Thai malaria case data was acquired from the World Health Organization (WHO) from 2005 to 2009. Using Google correlate, an openly available online tool, and by surveying Thai physicians, search queries potentially related to malaria prevalence were identified. Four linear regression models were built from different sub-sets of malaria-related queries to be used in future predictions. The models’ accuracies were evaluated by their ability to predict the malaria outbreak in 2009, their correlation with the entire available malaria case data, and by Akaike information criterion (AIC).ResultsEach model captured the bulk of the variability in officially reported malaria incidence. Correlation in the validation set ranged from 0.75 to 0.92 and AIC values ranged from 808 to 586 for the models. While models using malaria-related and general health terms were successful, one model using only microscopy-related terms obtained equally high correlations to malaria case data trends. The model built strictly of queries provided by Thai physicians was the only one that consistently captured the well-documented second seasonal malaria peak in Thailand.ConclusionsModels built from Google search queries were able to adequately estimate malaria activity trends in Thailand, from 2005–2010, according to official malaria case counts reported by WHO. While presenting their own limitations, these search queries may be valid real-time indicators of malaria incidence in the population, as correlations were on par with those of related studies for other infectious diseases. Additionally, this methodology provides a cost-effective description of malaria prevalence that can act as a complement to traditional public health surveillance. This and future studies will continue to identify ways to leverage web-based data to improve public health.

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Section: Discussionmentioning

confidence: 78%

Using search queries for malaria surveillance, Thailand

Ocampo

Chunara

Brownstein

2013

Malar J

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“…Claims need to be carefully critiqued, as nearly all of the literature on digital disease detection 4,6,[9][10][11][12][13][14]41 relies on weak methodologic approaches or patterns of association similar to GFT, with rare exception. 5,[42][43][44][45] Thus, investigators should turn to more sophisticated approaches for the development and evaluation of digital systems.…”

Section: Implications For Digital Disease Detectionmentioning

confidence: 99%

What Can Digital Disease Detection Learn from (an External Revision to) Google Flu Trends?

Santillana

Zhang

Althouse

et al. 2014

American Journal of Preventive Medicine

162

174

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“…1,7 Using Internet data for predicting the occurrence of both communicable and noncommunicable diseases has been shown to be reliable, eg, influenza outbreaks, and kidney stone occurrence. [8][9][10][11][12][13][14] The Swedish Institute for Communicable Disease Control has an automated surveillance system that uses Internet queries on the medical Web site the Stockholm Health Care Guide for epidemiologic surveillance. 10 The use of Internet data has also proven to be an effective tool with which to predict outcome in other fields, eg, the stock market and book and movie sales.…”

Section: Introduction Backgroundmentioning

confidence: 99%