The multivariate Bayesian scan statistic (MBSS) enables timely detection and characterization of emerging events by integrating multiple data streams. MBSS can model and differentiate between multiple event types: it uses Bayes’ Theorem to compute the posterior probability that each event type Ek has affected each space-time region S. Results are visualized using a ‘posterior probability map’ showing the total probability that each location has been affected. Although the original MBSS method assumes a uniform prior over circular regions, and thus loses power to detect elongated and irregular clusters, our Fast Subset Sums (FSS) method assumes a hierarchical prior, which assigns non-zero prior probabilities to every subset of locations, substantially improving detection power and accuracy for irregular regions.
Objective
We propose a new, computationally efficient Bayesian method for detection and visualization of irregularly shaped clusters. This Generalized Fast Subset Sums (GFSS) method extends our recently proposed MBSS and FSS approaches, and substantially improves timeliness and accuracy of event detection.