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) a...

• Type: Conference
• Year: 2005
• Where: KDD
• Authors: Daniel B. Neill, Andrew W. Moore, Maheshkumar Sabhnani, Kenny Daniel
• Keywords: Cluster Detection Methods, Data Mining, KDD 2005, Keywords Cluster Detection, Space-time Scan Statistics