@article{lampos2016assessing,
author = {Lampos, Vasileios and Yom-Tov, Elad and Pebody, Richard and Cox, Ingemar J.},
title = {Assessing the Impact of a Health Intervention via User-generated Internet Content},
year = {2016},
month = {September},
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 usergenerated 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 in uenza 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.},
publisher = {Springer},
url = {http://approjects.co.za/?big=en-us/research/publication/assessing-impact-health-intervention-via-user-generated-internet-content/},
journal = {Data mining and knowledge discovery},
edition = {Data mining and knowledge discovery},
}