Henry Kautz, the Robin and Tim Wentworth Director of the Goergen Institute for Data Science, is data mining social media such as Twitter to identify global disease outbreaks in their earliest stages and track their spread.
The approach has the potential to dwarf previous methods for health monitoring in scalability and immediacy and is changing how we predict, monitor, and address health issues.
Kautz and Adam Sadilek, a former postdoctoral fellow who now works at Google, demonstrated that they could predict which Twitter users would get the flu—up to eight days in advance—by “mining” the social media network for tweets of people reporting symptoms in the New York City area. They used the GPS tags embedded in the tweets sent from cell phones to track those persons’ encounters with other Twitter users, whose own risks of becoming ill could then be calculated and tested.
Kautz is confident this approach “could give researchers, medical professionals, and organizations like the Centers for Disease Control a sort of early warning system that could be applicable to all kinds of disease outbreaks.” In addition, the data can also be mined to help answer fundamental questions, such as how large-scale epidemics emerge from low-level interactions between people in the course of their everyday lives.