Abstract #301929

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JSM 2003 Abstract #301929
Activity Number: 436
Type: Topic Contributed
Date/Time: Thursday, August 7, 2003 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #301929
Title: Alternative Approaches for Syndromic Surveillance
Author(s): Ikuho Yamada*+ and Peter A. Rogerson
Companies: University of Buffalo and University of Buffalo
Address: Dept. of Geography, Buffalo, NY, 14261-0001,
Keywords: Poisson cusum ; overdispersion ; surveillance
Abstract:

The ongoing monitoring of public health may often require methods appropriate for data consisting of time-indexed, multiregional vectors of small counts of the number of individual with particular syndromes. One natural approach is to use cumulative sum methods developed for Poisson variables. In adopting this method, several questions arise. Because deviations from expectations are used in the cumulative sum, an appropriate model for expectations must be developed. How should available historical data optimally fade away with time? How should changes in model parameters be tracked and updated? In addition, the common presence of overdispersion implies that the Poisson model will be ineffective. I will explore the use of a negative binomial cumulative sum approach to account for the overdispersion. The approaches will be illustrated using data on census tract counts of clinic visits for lower respiratory tract infections in the Boston area.

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