We propose to develop software can cluster statistics appropriate when
the exact space-time location of health events are known. Health
professionals are investigating an increasing number of possible disease
clusters, and statistical tests play an important role in cluster
description and analysis. Existing cluster statistics assume precise
data, when in reality health events are often imprecise (e.g. place-of-
residence is known only to the census district or zip-code) and uncertain
(e.g. 'I first became ill sometime in 1985'). Most cluster statistics can
be written as the cross product of two matrices where one matrix reflects
nearest neighbor, distance of adjacency relationships and the second
matrix is health related (e.g. case-control identities). This research
will explore a general approach to clustering which incorporates
uncertainty regarding space-time locations into the nearest neighbor,
distance or adjacency relationship. Because the approach is general the
proposed methods can be used with almost all exiting cluster tests. In
phase 1 we will determine feasibility by implementing this general
approach for Cuzick & Edwards (nearest neighbor-based), Mantel's
(distance-based) and Knox's (adjacency-based) tests. The delivery of the
prototype software and Manual at the end of phase 1 will be the criterion
for demonstrating project feasibility. In phase 2 we will extend the
approach to 10 other cluster tests and evaluate the fuzzy clustering
algorithms using statistical power comparisons based on 3 realistic
PROPOSED COMMERCIAL APPLICATION: The resulting software will be a
powerful tool for the statistical description and detection of realistic
clusters of health events characterized by uncertain space-time
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