Show simple item record

dc.contributor.advisorFricker, Ronald D.
dc.contributor.authorMatthew C. Knitt.
dc.date.accessioned2012-03-14T17:38:20Z
dc.date.available2012-03-14T17:38:20Z
dc.date.issued2007-06
dc.identifier.urihttp://hdl.handle.net/10945/3417
dc.description.abstractBiological 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.en_US
dc.description.urihttp://archive.org/details/acomparativenaly109453417
dc.format.extentxx, 73 p. : ill. ;en_US
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.subject.lcshBioterrorismen_US
dc.subject.lcshDiseasesen_US
dc.titleA comparative analysis of multivariate statistical detection methods applied to syndromic surveillanceen_US
dc.typeThesisen_US
dc.contributor.secondreaderOlwell, David H.
dc.contributor.corporateNaval Postgraduate School (U.S.)
dc.contributor.departmentApplied Science (Operations Research)
dc.description.serviceUS Navy (USN) authors.en_US
dc.identifier.oclc160107395
etd.thesisdegree.nameM.S.en_US
etd.thesisdegree.disciplineApplied Science (Operations Research)en_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
etd.verifiednoen_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record