Fast Multidimensional Subset Scan for Outbreak Detection and Characterization

Authors

  • Daniel B. Neill Event and Pattern Detection Laboratory, Carnegie Mellon University
  • Tarun Kumar Event and Pattern Detection Laboratory, Carnegie Mellon University

DOI:

https://doi.org/10.5210/ojphi.v5i1.4391

Abstract

Multidimensional Subset Scan (MD-Scan) is a new method for early outbreak detection and characterization using multivariate case data from individuals in a population. MD-Scan extends previous work on multivariate event detection by identifying the characteristics of the affected subpopulation (e.g. affected gender(s), age groups, and behavioral risk factors), and enables more timely and more accurate detection while maintaining computational tractability.

Author Biography

Daniel B. Neill, Event and Pattern Detection Laboratory, Carnegie Mellon University

Daniel B. Neill is Associate Professor of Information Systems at Carnegie Mellon University's Heinz College, where he directs the Event and Pattern Detection Laboratory. His work focuses on new methods for timely, accurate, and scalable event detection in massive datasets. He was Scientific Program Chair of the 2011 ISDS Annual Conference. A receipient of the NSF CAREER Award, he was recently named one of the top ten 'researchers to watch' in artificial intelligence.

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Published

2013-03-23

How to Cite

Neill, D. B., & Kumar, T. (2013). Fast Multidimensional Subset Scan for Outbreak Detection and Characterization. Online Journal of Public Health Informatics, 5(1). https://doi.org/10.5210/ojphi.v5i1.4391

Issue

Vol. 5 No. 1 (2013)

Section

Oral Presentations: Temporal or Spatio-temporal

License

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