Detection of Outbreaks in Syndromic Surveillance Data Using Monotonic Regression
December 2006
Jared Burdin, The MITRE Corporation
Mojdeh Mohtashemi, The MITRE Corporation, MIT Computer Science and AI Lab
Martin Kulldorff, Harvard Medical School and Harvard Pilgrim Health Care
James Dunyak, The MITRE Corporation
ABSTRACT
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 likelihood ratio test in which both the null
and alternative hypotheses are normally distributed.
The data is daily counts of interactions between
patients and the National Bioterrorism Syndromic
Surveillance Demonstration Program 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 five-day windows of data. For each
window a test statistic is computed and an outbreak is
indicated if it exceeds a threshold.
Additional Search Keywords
Syndromic surveillance, early outbreak
detection, generalized likelihood ratio test, pool-adjacent-violators algorithm
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