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

作者：Lampos Vasileios^*; Yom Tov Elad; Pebody Richard; Cox Ingemar J

来源：Data Mining and Knowledge Discovery, 2015, 29(5): 1434-1457.

DOI：10.1007/s10618-015-0427-9

• Gaussian Process
• Infectious diseases
• Intervention
• Search query logs
• Social media
• Supervised learning
• User-generated content

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.

• 出版日期2015-9
• 单位Microsoft