The Library
Google trends can improve surveillance of Type 2 diabetes
Tools
Tools
Tools
Tkachenko, Nataliya, Chotvijit, Sarunkorn, Gupta, Neha, Bradley, Emma, Gilks, Charlotte, Guo, Weisi, Crosby, Henry James, Shore, Eliot, Thiarai, Malkiat, Procter, Rob and Jarvis, Stephen A. (2017) Google trends can improve surveillance of Type 2 diabetes. Scientific Reports, 7 . 4993. doi:10.1038/s41598-017-05091-9
|
PDF
WRAP-Google-trends-improve-surveillance-Type 2-diabetes-Jarvis-2017.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (3902Kb) | Preview |
|
![[img]](http://wrap.warwick.ac.uk/style/images/fileicons/application_pdf.png) |
PDF
WRAP-Google-trends-improve-diabetes-Tkachenko-2017.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (10Mb) |
Official URL: https://doi.org/10.1038/s41598-017-05091-9
Abstract
Recent studies demonstrate that people are increasingly looking online to assess their health, with reasons varying from personal preferences and beliefs to inability to book a timely appointment with their local medical practice. Records of these activities represent a new source of data about the health of populations, but which is currently unaccounted for by disease surveillance models. This could potentially be useful as evidence of individuals’ perception of bodily changes and self-diagnosis of early symptoms of an emerging disease. We make use of the Experian geodemographic Mosaic dataset in order to extract Type 2 diabetes candidate risk variables and compare their temporal relationships with the search keywords, used to describe early symptoms of the disease on Google. Our results demonstrate that Google Trends can detect early signs of diabetes by monitoring combinations of keywords, associated with searches for hypertension treatment and poor living conditions; Combined search semantics, related to obesity, how to quit smoking and improve living conditions (deprivation) can be also employed, however, may lead to less accurate results.
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Divisions: | Faculty of Science > Computer Science Faculty of Science > Mathematics |
||||||
| Journal or Publication Title: | Scientific Reports | ||||||
| Publisher: | Nature Publishing Group | ||||||
| ISSN: | 2045-2322 | ||||||
| Official Date: | 10 July 2017 | ||||||
| Dates: |
|
||||||
| Date of first compliant deposit: | 21 June 2017 | ||||||
| Volume: | 7 | ||||||
| Article Number: | 4993 | ||||||
| DOI: | 10.1038/s41598-017-05091-9 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
