document: Detection of emerging space-time clusters We propose a new class of spatio-temporal cluster detection methods designed for the rapid detection of emerging space-time clusters . We focus on the motivating application of prospective disease surveillance : detecting space-time clusters of disease cases resulting from an emerging disease outbreak . Automatic , real-time detection of outbreaks can enable rapid epidemiological response , potentially reducing rates of morbidity and mortality . Building on the prior work on spatial and space-time scan statistics , our methods combine time series analysis ( to determine how many cases we expect to observe for a given spatial region in a given time interval ) with new `` emerging cluster '' space-time scan statistics ( to decide whether an observed increase in cases in a region is significant ) , enabling fast and accurate detection of emerging outbreaks . We evaluate these methods on two types of simulated outbreaks : aerosol release of inhalational anthrax ( e.g. from a bioterrorist attack ) and FLOO ( `` Fictional Linear Onset Outbreak '' ) , injected into actual baseline data ( Emergency Department records and over-the-counter drug sales data from Allegheny County ) . We demonstrate that our methods are successful in rapidly detecting both outbreak types while keeping the number of false positives low , and show that our new `` emerging cluster '' scan statistics consistently outperform the standard `` persistent cluster '' scan statistics approach .
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