Literature DB >> 24326584

A comparison of Internet search trends and sexually transmitted infection rates using Google trends.

Amy K Johnson1, Supriya D Mehta.   

Abstract

Google Trends was used to determine the relationship between sexually transmitted infection (STI)-related search engine trends and STI rates. Trends seem to be similar to the relative rates of STIs and to regional differences in rates. Search engine trends are an innovative tool to integrate into STI surveillance.

Entities:  

Mesh：

Year:  2014        PMID： 24326584     DOI： 10.1097/OLQ.0000000000000065

Source DB:  PubMed          Journal:  Sex Transm Dis        ISSN： 0148-5717            Impact factor:   2.830


  14 in total

1.  Measuring Global Disease with Wikipedia: Success, Failure, and a Research Agenda.

Authors:  Reid Priedhorsky; Dave Osthus; Ashlynn R Daughton; Kelly R Moran; Nicholas Generous; Geoffrey Fairchild; Alina Deshpande; Sara Y Del Valle
Journal:  CSCW Conf Comput Support Coop Work       Date:  2017 Feb-Mar

2.  Using Search Engine Data as a Tool to Predict Syphilis.

Authors:  Sean D Young; Elizabeth A Torrone; John Urata; Sevgi O Aral
Journal:  Epidemiology       Date:  2018-07       Impact factor: 4.822

3.  Utilizing Big Data From Google Trends to Map Population Depression in the United States: Exploratory Infodemiology Study.

Authors:  Alex Wang; Robert McCarron; Daniel Azzam; Annamarie Stehli; Glen Xiong; Jeremy DeMartini
Journal:  JMIR Ment Health       Date:  2022-03-31

4.  The utility of Google Trends data to examine interest in cancer screening.

Authors:  M Schootman; A Toor; P Cavazos-Rehg; D B Jeffe; A McQueen; J Eberth; N O Davidson
Journal:  BMJ Open       Date:  2015-06-08       Impact factor: 2.692

5.  The use of google trends in health care research: a systematic review.

Authors:  Sudhakar V Nuti; Brian Wayda; Isuru Ranasinghe; Sisi Wang; Rachel P Dreyer; Serene I Chen; Karthik Murugiah
Journal:  PLoS One       Date:  2014-10-22       Impact factor: 3.240

6.  Disease surveillance based on Internet-based linear models: an Australian case study of previously unmodeled infection diseases.

Authors:  Florian Rohart; Gabriel J Milinovich; Simon M R Avril; Kim-Anh Lê Cao; Shilu Tong; Wenbiao Hu
Journal:  Sci Rep       Date:  2016-12-20       Impact factor: 4.379

7.  Examining the themes of STD-related Internet searches to increase specificity of disease forecasting using Internet search terms.

Authors:  Amy K Johnson; Tarek Mikati; Supriya D Mehta
Journal:  Sci Rep       Date:  2016-11-09       Impact factor: 4.379

8.  Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review.

Authors:  Amaryllis Mavragani; Gabriela Ochoa; Konstantinos P Tsagarakis
Journal:  J Med Internet Res       Date:  2018-11-06       Impact factor: 5.428

9.  The application rate for urology specialty compared with other specialties from 2007 to 2014 in Korea: is it influenced by social interest manifested by internet trends?

Authors:  Hwa Yeon Sun; Young Myoung Ko; Seung Wook Lee; Bora Lee; Jae Heon Kim
Journal:  BMC Urol       Date:  2018-07-24       Impact factor: 2.264

10.  Using internet search data to predict new HIV diagnoses in China: a modelling study.

Authors:  Qingpeng Zhang; Yi Chai; Xiaoming Li; Sean D Young; Jiaqi Zhou
Journal:  BMJ Open       Date:  2018-10-17       Impact factor: 2.692
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.