Fast subset scan for spatial pattern detection

Research output: Contribution to journalArticlepeer-review

Abstract

We propose a new 'fast subset scan' approach for accurate and computationally efficient event detection in massive data sets. We treat event detection as a search over subsets of data records, finding the subset which maximizes some score function. We prove that many commonly used functions (e.g. Kulldorff's spatial scan statistic and extensions) satisfy the 'linear time subset scanning' property, enabling exact and efficient optimization over subsets. In the spatial setting, we demonstrate that proximity-constrained subset scans substantially improve the timeliness and accuracy of event detection, detecting emerging outbreaks of disease 2 days faster than existing methods.

Original languageEnglish (US)
Pages (from-to)337-360
Number of pages24
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Volume74
Issue number2
DOIs
StatePublished - Mar 2012

Keywords

  • Algorithms
  • Disease surveillance
  • Event detection
  • Scan statistics
  • Spatial scan

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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