Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)
Daniel Neill, Andrew Moore, Gregory Cooper
We propose a new Bayesian method for spatial cluster detection, the “Bayesian spatial scan statistic,” and compare this method to the standard (frequentist) scan statistic approach. We demonstrate that the Bayesian statistic has several advantages over the frequentist approach, including increased power to detect clusters and (since randomization testing is unnecessary) much faster runtime. We evaluate the Bayesian and fre- quentist methods on the task of prospective disease surveillance: detect- ing spatial clusters of disease cases resulting from emerging disease out- breaks. We demonstrate that our Bayesian methods are successful in rapidly detecting outbreaks while keeping number of false positives low.