A comparative analysis of multivariate statistical detection methods applied to syndromic surveillance
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Authors
Matthew C. Knitt.
Subjects
Advisors
Fricker, Ronald D.
Date of Issue
2007-06
Date
Publisher
Monterey, California. Naval Postgraduate School
Language
Abstract
Biological terrorism is a threat to the security and well-being of the United States. It is critical to detect the presence of these attacks in a timely manner, in order to provide sufficient and effective responses to minimize or contain the damage inflicted. Syndromic surveillance is the process of monitoring public health-related data and applying statistical tests to determine the potential presence of a disease outbreak in the observed system. Our research involved a comparative analysis of two multivariate statistical methods, the multivariate CUSUM (MCUSUM) and the multivariate exponentially weighted moving average (MEWMA), both modified to look only for increases in disease incidence. While neither of these methods is currently in use in a biosurveillance system, they are among the most promising multivariate methods for this application. Our analysis was based on a series of simulations using synthetic syndromic surveillance data that mimics various types of background disease incidence and outbreaks. We found that, similar to results for the univariate CUSUM and EWMA, the directionally-sensitive MCUSUM and MEWMA perform very similarly.
Type
Thesis
Description
Series/Report No
Department
Applied Science (Operations Research)
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
Format
xx, 73 p. : ill. ;
Citation
Distribution Statement
Approved for public release; distribution is unlimited.