TY  - JOUR
KW  - Gaussian Process
KW  -  Infectious diseases
KW  -  Intervention
KW  -  Search query logs
KW  -  Social media
KW  -  Supervised learning
KW  -  User-generated content
PB  - Kluwer Academic Publishers
N2  - 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.
ID  - discovery1469803
AV  - public
Y1  - 2015/07/02/
EP  - 1457
TI  - Assessing the impact of a health intervention via user-generated Internet content
A1  - Lampos, V
A1  - Yom-Tov, E
A1  - Pebody, R
A1  - Cox, IJ
JF  - Data Mining and Knowledge Discovery
UR  - http://dx.doi.org/10.1007/s10618-015-0427-9
SN  - 1384-5810
IS  - 5
N1  - © 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.
SP  - 1434
VL  - 29
ER  -