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Activity Number: 144
Type: Contributed
Date/Time: Monday, August 7, 2006 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #307392
Title: A Wavelet-Based Method for the Prospective Monitoring of Disease Incidence Counts in Space and Time
Author(s): J. Brooke Marshall*+ and Dan Spitzner and William H. Woodall
Companies: Virginia Polytechnic Institute and State University and Virginia Polytechnic Institute and State University and Virginia Polytechnic Institute and State University
Address: 403 Progress Street, NE, Blacksburg, VA, 24060,
Keywords: public health surveillance ; wavelets ; Poisson regression ; disease clusters ; control charts ; ARL performance

In epidemiology it is useful to monitor disease occurrences prospectively to allow for quicker detection of disease clusters. Here we present a prospective method for monitoring disease occurrences in a geographical region. In this method, a surface of incidence counts is modeled over time in the region of interest. This surface is modeled using Poisson regression where the regressors are functions from the Haar wavelet basis. The surface is estimated each time incidence data is obtained using both past and current observations, weighing current observations more heavily. The flexibility of this method allows for the detection of changes in the incidence surface, increases in the overall mean incidence count, and clusters of disease within smaller areas of the region, by using control charts. The control limits for these charts are determined by average run length performance.

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