JSM 2005 Online Program

Abstract #304466

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	326
Type:	Contributed
Date/Time:	Tuesday, August 9, 2005 : 2:00 PM to 3:50 PM
Sponsor:	Section on Statistical Graphics
Abstract - #304466
Title:	Cluster Detection and Visualization via Tomographic Methods
Author(s):	Al Ozonoff*+ and Marcello Pagano and Laura Forsberg and Caroline Jeffery
Companies:	Boston University and Harvard School of Public Health and Harvard School of Public Health and Harvard School of Public Health
Address:	715 Albany Street T4E, Boston, MA, 02118, United States
Keywords:	Syndromic surveillance ; Statistical graphics ; Density estimation ; Spatio-temporal disease patterns
Abstract:	Detecting aberrations in spatial data (e.g., infectious disease surveillance data) is only one component of an effective surveillance system. We also must determine the boundaries of a possible cluster, especially if these boundaries change over time. This problem can be challenging because the underlying population (under an assumption of no clustering) will be highly heterogeneous. Traditional density estimation methods may not perform well under such circumstances. We present an alternative nonparametric approach to locating and mapping spatial clusters of disease, following a method with mathematical roots in tomographic image processing. This method has been successfully applied to syndromic surveillance data to create dynamic maps that illustrate the spatiotemporal patterns of disease.

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