Knowing when and where diseases such as the flu will strike and their expected severity can save lives, save money and improve healthcare for millions of people, and that’s the focus of the Oak Ridge Bio-surveillance Toolkit, or ORBiT. This collection of novel statistical and machine learning tools, developed by Oak Ridge National Laboratory’s Laura Pullum and Arvind Ramanathan, integrates datasets from prescription drug sales records and social media such as Twitter. “Through this approach, we can bound the confidence in the statistical predictions – much like the cone of uncertainty in hurricane forecasting – of where diseases might strike,” Pullum said. ORBiT will ultimately provide decision-makers with a tool that maximizes their ability to forecast biological threats, reduce false alarms and prevent and treat diseases.
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