File:A comparative analysis of multivariate statistical detection methods applied to syndromic surveillance (IA acomparativenaly109453417).pdf
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Summary[edit]
A comparative analysis of multivariate statistical detection methods applied to syndromic surveillance ( ) | |
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Author |
Matthew C. Knitt. |
Title |
A comparative analysis of multivariate statistical detection methods applied to syndromic surveillance |
Publisher |
Monterey, California. Naval Postgraduate School |
Description |
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. Subjects: Bioterrorism; Diseases |
Language | English |
Publication date | June 2007 |
Current location |
IA Collections: navalpostgraduateschoollibrary; fedlink |
Accession number |
acomparativenaly109453417 |
Source | |
Permission (Reusing this file) |
Approved for public release, distribution unlimited |
Licensing[edit]
Public domainPublic domainfalsefalse |
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This file has been identified as being free of known restrictions under copyright law, including all related and neighboring rights. |
https://creativecommons.org/publicdomain/mark/1.0/PDMCreative Commons Public Domain Mark 1.0falsefalse
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current | 21:18, 13 July 2020 | 1,275 × 1,650, 96 pages (1.49 MB) | Fæ (talk | contribs) | FEDLINK - United States Federal Collection acomparativenaly109453417 (User talk:Fæ/IA books#Fork8) (batch 1993-2020 #5178) |
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Short title | A comparative analysis of multivariate statistical detection methods applied to syndromic surveillance |
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Author | Matthew C. Knitt. |
Software used | Matthew C. Knitt. |
Conversion program | Microsoft® Office Word 2007 |
Encrypted | no |
Page size | 612 x 792 pts (letter) |
Version of PDF format | 1.4 |