Skip to Main Content
HBS Home
  • About
  • Academic Programs
  • Alumni
  • Faculty & Research
  • Baker Library
  • Giving
  • Harvard Business Review
  • Initiatives
  • News
  • Recruit
  • Map / Directions
Faculty & Research
  • Faculty
  • Research
  • Featured Topics
  • Academic Units
  • …→
  • Harvard Business School→
  • Faculty & Research→
Publications
Publications
  • Article
  • Statistics in Medicine

Fast Subset Scan for Multivariate Spatial Biosurveillance

By: Daniel B. Neill, Edward McFowland III and Huanian Zheng
  • Format:Print
ShareBar

Abstract

We present new subset scan methods for multivariate event detection in massive space-time datasets. We extend the recently proposed 'fast subset scan' framework from univariate to multivariate data, enabling computationally efficient detection of irregular space-time clusters even when the numbers of spatial locations and data streams are large. For two variants of the multivariate subset scan, we demonstrate that the scan statistic can be efficiently optimized over proximity-constrained subsets of locations and over all subsets of the monitored data streams, enabling timely detection of emerging events and accurate characterization of the affected locations and streams. Using our new fast search algorithms, we perform an empirical comparison of the Subset Aggregation and Kulldorff multivariate subset scans on synthetic data and real-world disease surveillance tasks, demonstrating tradeoffs between the detection and characterization performance of the two methods.

Keywords

Algorithms; Disease Surveillance; Event Detection; Scan Statistics; Spatial Scan

Citation

Neill, Daniel B., Edward McFowland III, and Huanian Zheng. "Fast Subset Scan for Multivariate Spatial Biosurveillance." Statistics in Medicine 32, no. 13 (June 15, 2013): 2185–2208.
  • Find it at Harvard
  • Purchase

About The Author

Edward McFowland III

Technology and Operations Management
→More Publications

More from the Authors

    • October–December 2022
    • INFORMS Journal on Data Science

    Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem

    By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
    • 2022
    • Journal of Computational and Graphical Statistics

    Nonparametric Subset Scanning for Detection of Heteroscedasticity

    By: Charles R. Doss and Edward McFowland III
    • Pattern Recognition Letters

    Pattern Detection in the Activation Space for Identifying Synthesized Content

    By: Celia Cintas, Skyler Speakman, Girmaw Abebe Tadesse, Victor Akinwande, Edward McFowland III and Komminist Weldemariam
More from the Authors
  • Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
  • Nonparametric Subset Scanning for Detection of Heteroscedasticity By: Charles R. Doss and Edward McFowland III
  • Pattern Detection in the Activation Space for Identifying Synthesized Content By: Celia Cintas, Skyler Speakman, Girmaw Abebe Tadesse, Victor Akinwande, Edward McFowland III and Komminist Weldemariam
ǁ
Campus Map
Harvard Business School
Soldiers Field
Boston, MA 02163
→Map & Directions
→More Contact Information
  • Make a Gift
  • Site Map
  • Jobs
  • Harvard University
  • Trademarks
  • Policies
  • Accessibility
  • Digital Accessibility
Copyright © President & Fellows of Harvard College