eprintid: 1469803
rev_number: 39
eprint_status: archive
userid: 608
dir: disk0/01/46/98/03
datestamp: 2015-10-09 15:01:00
lastmod: 2021-09-26 22:49:14
status_changed: 2015-10-09 15:01:00
type: article
metadata_visibility: show
item_issues_count: 0
creators_name: Lampos, V
creators_name: Yom-Tov, E
creators_name: Pebody, R
creators_name: Cox, IJ
title: Assessing the impact of a health intervention via user-generated Internet content
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: Gaussian Process, Infectious diseases, Intervention, Search query logs, Social media, Supervised learning, User-generated content
note: © The Author(s) 2015. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
abstract: Assessing the effect of a health-oriented intervention by traditional epidemiological methods is commonly based only on population segments that use healthcare services. Here we introduce a complementary framework for evaluating the impact of a targeted intervention, such as a vaccination campaign against an infectious disease, through a statistical analysis of user-generated content submitted on web platforms. Using supervised learning, we derive a nonlinear regression model for estimating the prevalence of a health event in a population from Internet data. This model is applied to identify control location groups that correlate historically with the areas, where a specific intervention campaign has taken place. We then determine the impact of the intervention by inferring a projection of the disease rates that could have emerged in the absence of a campaign. Our case study focuses on the influenza vaccination program that was launched in England during the 2013/14 season, and our observations consist of millions of geo-located search queries to the Bing search engine and posts on Twitter. The impact estimates derived from the application of the proposed statistical framework support conventional assessments of the campaign.
date: 2015-07-02
publisher: Kluwer Academic Publishers
official_url: http://dx.doi.org/10.1007/s10618-015-0427-9
vfaculties: VENG
oa_status: green
full_text_type: pub
primo: open
primo_central: open_green
article_type_text: Article in Press
verified: verified_manual
elements_source: Manually entered
elements_id: 1041180
doi: 10.1007/s10618-015-0427-9
lyricists_name: Cox, Ingemar
lyricists_name: Lampos, Vasileios
lyricists_id: IJCOX77
lyricists_id: VLAMP72
full_text_status: public
publication: Data Mining and Knowledge Discovery
volume: 29
number: 5
pagerange: 1434-1457
issn: 1384-5810
citation:        Lampos, V;    Yom-Tov, E;    Pebody, R;    Cox, IJ;      (2015)    Assessing the impact of a health intervention via user-generated Internet content.                   Data Mining and Knowledge Discovery , 29  (5)   pp. 1434-1457.    10.1007/s10618-015-0427-9 <https://doi.org/10.1007/s10618-015-0427-9>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/1469803/13/Assessing%20the%20impact%20of%20a%20health%20intervention%20via%20user-generated%20Internet%20content.pdf