Learning Outbreak Regions for Bayesian Spatial Biosurveillance

This work incorporates model learning into a Bayesian framework for outbreak detection. Our method learns the spatial characteristics of each outbreak type from a small number of labeled training examples, assuming a generative outbreak model with latent center. We show that using the learned models to calculate prior probabilities for a Bayesian scan statistic significantly improves detection performance.