Literature DB >> 19845471

Google trends: a web-based tool for real-time surveillance of disease outbreaks.

Herman Anthony Carneiro1, Eleftherios Mylonakis.   

Abstract

Google Flu Trends can detect regional outbreaks of influenza 7-10 days before conventional Centers for Disease Control and Prevention surveillance systems. We describe the Google Trends tool, explain how the data are processed, present examples, and discuss its strengths and limitations. Google Trends shows great promise as a timely, robust, and sensitive surveillance system. It is best used for surveillance of epidemics and diseases with high prevalences and is currently better suited to track disease activity in developed countries, because to be most effective, it requires large populations of Web search users. Spikes in search volume are currently hard to interpret but have the benefit of increasing vigilance. Google should work with public health care practitioners to develop specialized tools, using Google Flu Trends as a blueprint, to track infectious diseases. Suitable Web search query proxies for diseases need to be established for specialized tools or syndromic surveillance. This unique and innovative technology takes us one step closer to true real-time outbreak surveillance.

Entities:  

Mesh:

Year:  2009        PMID: 19845471     DOI: 10.1086/630200

Source DB:  PubMed          Journal:  Clin Infect Dis        ISSN: 1058-4838            Impact factor:   9.079


  167 in total

1.  Does social selection explain the association between state-level racial animus and racial disparities in self-rated health in the United States?

Authors:  Sarah McKetta; Mark L Hatzenbuehler; Charissa Pratt; Lisa Bates; Bruce G Link; Katherine M Keyes
Journal:  Ann Epidemiol       Date:  2017-07-13       Impact factor: 3.797

2.  Use of Google in study of noninfectious medical conditions.

Authors:  Benjamin N Breyer; Michael L Eisenberg
Journal:  Epidemiology       Date:  2010-07       Impact factor: 4.822

3.  Adoption of a national antimicrobial guide (SWAB-ID) in the Netherlands.

Authors:  Emelie C Schuts; Caroline M van den Bosch; Inge C Gyssens; Bart-Jan Kullberg; Maurine A Leverstein-van Hall; Stephanie Natsch; Fre Sebens; Martha B Adams; Richard Drew; Jan M Prins
Journal:  Eur J Clin Pharmacol       Date:  2015-10-22       Impact factor: 2.953

4.  "Google flu trends" and emergency department triage data predicted the 2009 pandemic H1N1 waves in Manitoba.

Authors:  Mohammad Tufail Malik; Abba Gumel; Laura H Thompson; Trevor Strome; Salaheddin M Mahmud
Journal:  Can J Public Health       Date:  2011 Jul-Aug

5.  Commentary: Epidemiology in the era of big data.

Authors:  Stephen J Mooney; Daniel J Westreich; Abdulrahman M El-Sayed
Journal:  Epidemiology       Date:  2015-05       Impact factor: 4.822

6.  The association of online search interest with polio cases and vaccine coverage: an infodemiological and ecological study.

Authors:  Elbert John V Layug; Adrian I Espiritu; Loudella V Calotes-Castillo; Roland Dominic G Jamora
Journal:  Eur J Pediatr       Date:  2021-03-27       Impact factor: 3.183

7.  Lymelight: forecasting Lyme disease risk using web search data.

Authors:  Adam Sadilek; Yulin Hswen; John S Brownstein; Evgeniy Gabrilovich; Shailesh Bavadekar; Tomer Shekel
Journal:  NPJ Digit Med       Date:  2020-02-04

8.  Gauging interest of the general public in laser-assisted in situ keratomileusis eye surgery.

Authors:  Joshua D Stein; David M Childers; Bin Nan; Shahzad I Mian
Journal:  Cornea       Date:  2013-07       Impact factor: 2.651

9.  Likely correlation between sources of information and acceptability of A/H1N1 swine-origin influenza virus vaccine in Marseille, France.

Authors:  Antoine Nougairède; Jean-Christophe Lagier; Laetitia Ninove; Catherine Sartor; Sékéné Badiaga; Elizabeth Botelho; Philippe Brouqui; Christine Zandotti; Xavier De Lamballerie; Bernard La Scola; Michel Drancourt; Ernest A Gould; Rémi N Charrel; Didier Raoult
Journal:  PLoS One       Date:  2010-06-25       Impact factor: 3.240

10.  Social network sensors for early detection of contagious outbreaks.

Authors:  Nicholas A Christakis; James H Fowler
Journal:  PLoS One       Date:  2010-09-15       Impact factor: 3.240

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