Predicting Disease Transmission from Geo-Tagged Micro-Blog Data

Researchers have begun to mine social network data in order to predict a variety of social, economic, and health related phenomena. While previous work has focused on predict- ing aggregate properties, such as the prevalence of seasonal influenza in a given country, researchers consider the task of fine- grained prediction of the health of specific people from noisy and incomplete data.
Authors construct a probabilistic model that can predict if and when an individual will fall ill with high precision and good recall on the basis of his social ties and co-locations with other people, as revealed by their Twitter posts. The model is highly scalable and can be used to predict general dynamic properties of individuals in large real world social networks. These results provide a foundation for research on fundamental questions of public health, including the identification of non-cooperative disease carriers (“Ty- phoid Marys”), adaptive vaccination policies, and our under- standing of the emergence of global epidemics from day-to- day interpersonal interactions.

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