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.
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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.
| Type: | Article |
|---|---|
| Title: | Assessing the impact of a health intervention via user-generated Internet content |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s10618-015-0427-9 |
| Publisher version: | http://dx.doi.org/10.1007/s10618-015-0427-9 |
| Additional information: | © 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. |
| Keywords: | Gaussian Process, Infectious diseases, Intervention, Search query logs, Social media, Supervised learning, User-generated content |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1469803 |
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