Assessing the impact of a health intervention via user-generated Internet content

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Assessing the impact of a health intervention via user-generated Internet content

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. Green open access

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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