A Bayesian spatial scan statistic (2006)

Daniel Neill, Andrew Moore, Gregory Cooper

Abstract

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.

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Approximate BibTeX Entry

@inproceedings{12345,
    Year = {2006},
    Volume = {18},
    Pages = {1003-1010},
    Booktitle = {Advances in Neural Information Processing Systems},
    Author = { Daniel Neill, Andrew Moore, Gregory Cooper },
    Title = {A Bayesian spatial scan statistic}
}