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- W2017805816 abstract "We propose a new class of spatio-temporal detection methods designed for the rapid detection of space-time clusters. We focus on the motivating application of prospective disease surveillance: detecting space-time clusters of disease cases resulting from an 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 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." @default.
- W2017805816 created "2016-06-24" @default.
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- W2017805816 date "2005-08-21" @default.
- W2017805816 modified "2023-09-26" @default.
- W2017805816 title "Detection of emerging space-time clusters" @default.
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- W2017805816 doi "https://doi.org/10.1145/1081870.1081897" @default.
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