Infection Hot Spot Mining from Social Media Trajectories

Abstract

Traditionally, in health surveillance, high risk zones are identified based only on the residence address or the working place of diseased individuals. This provides little information about the places where people are infected, the truly important information for disease control. The recent availability of spatial data generated by geotagged social media posts offers a unique opportunity: by identifying and following diseased individuals, we obtain a collection of sequential geo-located events, each sequence being issued by a social media user. The sequence of map positions implicitly provides an estimation of the users’ social trajectories as they drift on the map. The existing data mining techniques for spatial cluster detection fail to address this new setting as they require a single location to each individual under analysis. In this paper we present two stochastic models with their associated algorithms to mine this new type of data. The Visit Model finds the most likely zones that a diseased person visits, while the Infection Model finds the most likely zones where a person gets infected while visiting. We demonstrate the applicability and effectiveness of our proposed models by applying them to more than 100 million geotagged tweets from Brazil in 2015. In particular, we target the identification of infection hot spots associated with dengue, a mosquito-transmitted disease that affects millions of people in Brazil annually, and billions worldwide. We applied our algorithms to data from 11 large cities in Brazil and found infection hot spots, showing the usefulness of our methods for disease surveillance.