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Publication Detail
Assessing the impact of a health intervention via user-generated Internet content
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Article in Press
  • Authors:
    Lampos V, Yom-Tov E, Pebody R, Cox IJ
  • Publisher:
    Kluwer Academic Publishers
  • Publication date:
    02/07/2015
  • Journal:
    Data Mining and Knowledge Discovery
  • Status:
    Accepted
  • Print ISSN:
    1384-5810
  • Language:
    eng
  • Keywords:
    Gaussian Process, Infectious diseases, Intervention, Search query logs, Social media, Supervised learning, User-generated content
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.
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