Tracking Dynamic Water-borne Outbreaks with Temporal Consistency Constraints

Authors

  • Skyler Speakman Carnegie Mellon University
  • Yating Zhang Carnegie Mellon University
  • Daniel B. Neill Carnegie Mellon University

DOI:

https://doi.org/10.5210/ojphi.v5i1.4549

Abstract

We describe a novel graph-based event detection approach which can accurately identify and track dynamic outbreaks (where the affected region changes over time). Our approach enforces soft constraints on temporal consistency, allowing detected regions to grow, shrink, or move while penalizing implausible region dynamics. Using simulated contaminant plumes diffusing through a water distribution system, we demonstrate that our method improves both detection time and spatial-temporal accuracy when tracking dynamic water-borne outbreaks.

Author Biography

Skyler Speakman, Carnegie Mellon University

Skyler Speakman is a doctoral student at the Heinz College of Carnegie Mellon University. His research interests focus on the intersection of machine learning and public policy.

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Published

2013-03-24

How to Cite

Speakman, S., Zhang, Y., & Neill, D. B. (2013). Tracking Dynamic Water-borne Outbreaks with Temporal Consistency Constraints. Online Journal of Public Health Informatics, 5(1). https://doi.org/10.5210/ojphi.v5i1.4549

Issue

Section

Oral Presentations: Cluster Detection