A fast multi-resolution method for detection of significant spatial disease clusters

Daniel B. Neill, Andrew W. Moore

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Given an N×N grid of squares, where each square has a count and an underlying population, our goal is to find the square region with the highest density, and to calculate its significance by randomization. Any density measure D, dependent on the total count and total population of a region, can be used. For example, if each count represents the number of disease cases occurring in that square, we can use Kulldorff's spatial scan statistic DK to find the most significant spatial disease cluster. A naive approach to finding the maximum density region requires O(N3) time, and is generally computationally infeasible. We present a novel algorithm which partitions the grid into overlapping regions, bounds the maximum score of subregions contained in each region, and prunes regions which cannot contain the maximum density region. For sufficiently dense regions, this method finds the maximum density region in optimal O(N2) time, in practice resulting in significant (10-200x) speedups.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003
PublisherNeural information processing systems foundation
ISBN (Print)0262201526, 9780262201520
StatePublished - Jan 1 2004
Event17th Annual Conference on Neural Information Processing Systems, NIPS 2003 - Vancouver, BC, Canada
Duration: Dec 8 2003Dec 13 2003

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other17th Annual Conference on Neural Information Processing Systems, NIPS 2003
CountryCanada
CityVancouver, BC
Period12/8/0312/13/03

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Cite this

Neill, D. B., & Moore, A. W. (2004). A fast multi-resolution method for detection of significant spatial disease clusters. In Advances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003 (Advances in Neural Information Processing Systems). Neural information processing systems foundation.