Literature DB >> 20503183

The utility of "Google Trends" for epidemiological research: Lyme disease as an example.

Ari Seifter1, Alison Schwarzwalder, Kate Geis, John Aucott.   

Abstract

Internet search engines have become an increasingly popular resource for accessing health-related information. The key words used as well as the number and geographic location of searches can provide trend data, as have recently been made available by Google Trends. We report briefly on exploring this resource using Lyme disease as an example because it has well-described seasonal and geographic patterns. We found that search traffic for the string "Lyme disease" reflected increased likelihood of exposure during spring and summer months; conversely, the string "cough" had higher relative traffic during winter months. The cities and states with the highest amount of search traffic for "Lyme disease" overlapped considerably with those where Lyme is known to be endemic. Despite limitations to over-interpretation, we found Google Trends to approximate certain trends previously identified in the epidemiology of Lyme disease. The generation of this type of data may have valuable future implications in aiding surveillance of a broad range of diseases.

Entities:  

Mesh:

Year:  2010        PMID: 20503183     DOI: 10.4081/gh.2010.195

Source DB:  PubMed          Journal:  Geospat Health        ISSN: 1827-1987            Impact factor:   1.212


  65 in total

1.  "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

2.  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

3.  Novel surveillance of psychological distress during the great recession.

Authors:  John W Ayers; Benjamin M Althouse; Jon-Patrick Allem; Matthew A Childers; Waleed Zafar; Carl Latkin; Kurt M Ribisl; John S Brownstein
Journal:  J Affect Disord       Date:  2012-07-24       Impact factor: 4.839

4.  Using Search Engines to Investigate Shared Migraine Experiences.

Authors:  Sara M Burns; Dana P Turner; Katherine E Sexton; Hao Deng; Timothy T Houle
Journal:  Headache       Date:  2017-06-28       Impact factor: 5.887

Review 5.  Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives. Contribution of the IMIA Social Media Working Group.

Authors:  M M Hansen; T Miron-Shatz; A Y S Lau; C Paton
Journal:  Yearb Med Inform       Date:  2014-08-15

6.  Monitoring of non-cigarette tobacco use using Google Trends.

Authors:  Patricia A Cavazos-Rehg; Melissa J Krauss; Edward L Spitznagel; Ashley Lowery; Richard A Grucza; Frank J Chaloupka; Laura Jean Bierut
Journal:  Tob Control       Date:  2014-02-05       Impact factor: 7.552

7.  Seasonal effects on the occurrence of nocturnal leg cramps: a prospective cohort study.

Authors:  Scott R Garrison; Colin R Dormuth; Richard L Morrow; Greg A Carney; Karim M Khan
Journal:  CMAJ       Date:  2015-01-26       Impact factor: 8.262

8.  LESSONS LEARNED ABOUT PUBLIC HEALTH FROM ONLINE CROWD SURVEILLANCE.

Authors:  Shawndra Hill; Raina Merchant; Lyle Ungar
Journal:  Big Data       Date:  2013-09-10       Impact factor: 2.128

9.  A research agenda: does geocoding positional error matter in health GIS studies?

Authors:  Geoffrey M Jacquez
Journal:  Spat Spatiotemporal Epidemiol       Date:  2012-02-14

10.  Novel data sources for women's health research: mapping breast screening online information seeking through Google trends.

Authors:  Soudabeh Fazeli Dehkordy; Ruth C Carlos; Kelli S Hall; Vanessa K Dalton
Journal:  Acad Radiol       Date:  2014-07-04       Impact factor: 3.173

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