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Activity Number: 146
Type: Contributed
Date/Time: Monday, August 7, 2006 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #306832
Title: Detection of Outbreaks in Syndromic Surveillance Data Using Monotonic Regression
Author(s): Jared Burdin*+ and James Dunyak and Mojdeh Mohtashemi and Martin Kulldorff
Companies: The MITRE Corporation and The MITRE Corporation and The MITRE Corporation/MIT/AI Lab and Harvard Medical School/Harvard Pilgrim Health Care
Address: 202 Burlington Road, Bedford, MA, 01730,
Keywords: syndromic surveillance ; outbreak detection ; generalized maximum likelihood ratio test ; monotonic regression ; pool-adjacent-violators algorithm ; Poisson regression

Due to nonstationarity and substantial variability in outbreak profiles, early detection of disease outbreaks is challenging. In this paper we propose a method to detect outbreaks in syndromic surveillance data using a generalized maximum likelihood ratio test in which both the null and alternative hypotheses are Poisson distributed. The data is daily counts of interactions between patients and the Harvard Pilgrim Healthcare System in the Boston area. Using Poisson regression, we estimate the daily means and variances of the data as well as day of the week variations. The estimated means serve as the means under the null hypothesis. To determine the means under the alternative hypothesis we use a generalized form of the Pool-Adjacent-Violators algorithm on seven day windows of data. For each window a test statistic is computed and an outbreak is indicated if it exceeds a threshold.

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