Detection of emerging space-time clusters

Daniel Neill, Andrew W. Moore, Maheshkumar Sabhnani, Kenny Daniel

Research output: Contribution to conferencePaper

Abstract

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) 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.

Original languageEnglish (US)
Pages218-227
Number of pages10
DOIs
StatePublished - Dec 1 2005
EventKDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Chicago, IL, United States
Duration: Aug 21 2005Aug 24 2005

Other

OtherKDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
CountryUnited States
CityChicago, IL
Period8/21/058/24/05

Fingerprint

Statistics
Space surveillance
Time series analysis
Aerosols
Sales

Keywords

  • Biosurveillance
  • Cluster detection
  • Space-time scan statistics

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Neill, D., Moore, A. W., Sabhnani, M., & Daniel, K. (2005). Detection of emerging space-time clusters. 218-227. Paper presented at KDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, United States. https://doi.org/10.1145/1081870.1081897

Detection of emerging space-time clusters. / Neill, Daniel; Moore, Andrew W.; Sabhnani, Maheshkumar; Daniel, Kenny.

2005. 218-227 Paper presented at KDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, United States.

Research output: Contribution to conferencePaper

Neill, D, Moore, AW, Sabhnani, M & Daniel, K 2005, 'Detection of emerging space-time clusters', Paper presented at KDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, United States, 8/21/05 - 8/24/05 pp. 218-227. https://doi.org/10.1145/1081870.1081897
Neill D, Moore AW, Sabhnani M, Daniel K. Detection of emerging space-time clusters. 2005. Paper presented at KDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, United States. https://doi.org/10.1145/1081870.1081897
Neill, Daniel ; Moore, Andrew W. ; Sabhnani, Maheshkumar ; Daniel, Kenny. / Detection of emerging space-time clusters. Paper presented at KDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, United States.10 p.
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