A Bayesian spatial scan statistic

Daniel B. Neill, Andrew W. Moore, Gregory F. Cooper

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 frequentist methods on the task of prospective disease surveillance: detecting spatial clusters of disease cases resulting from emerging disease outbreaks. We demonstrate that our Bayesian methods are successful in rapidly detecting outbreaks while keeping number of false positives low.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference
Number of pages8
StatePublished - 2005
Event2005 Annual Conference on Neural Information Processing Systems, NIPS 2005 - Vancouver, BC, Canada
Duration: Dec 5 2005Dec 8 2005

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258


Other2005 Annual Conference on Neural Information Processing Systems, NIPS 2005
CityVancouver, BC

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


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