Syndromic surveillance using veterinary laboratory data: algorithm combination and customization of alerts.

<h4>Background</h4>Syndromic surveillance research has focused on two main themes: the search for data sources that can provide early disease detection; and the development of efficient algorithms that can detect potential outbreak signals.<h4>Methods</h4>This work combines t...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Fernanda C Dórea, Beverly J McEwen, W Bruce McNab, Javier Sanchez, Crawford W Revie
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a66fd7f0b81341a4b29a9e533aef586b
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a66fd7f0b81341a4b29a9e533aef586b
record_format	dspace
spelling	oai:doaj.org-article:a66fd7f0b81341a4b29a9e533aef586b2021-11-18T08:42:26ZSyndromic surveillance using veterinary laboratory data: algorithm combination and customization of alerts.1932-620310.1371/journal.pone.0082183https://doaj.org/article/a66fd7f0b81341a4b29a9e533aef586b2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24349216/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Syndromic surveillance research has focused on two main themes: the search for data sources that can provide early disease detection; and the development of efficient algorithms that can detect potential outbreak signals.<h4>Methods</h4>This work combines three algorithms that have demonstrated solid performance in detecting simulated outbreak signals of varying shapes in time series of laboratory submissions counts. These are: the Shewhart control charts designed to detect sudden spikes in counts; the EWMA control charts developed to detect slow increasing outbreaks; and the Holt-Winters exponential smoothing, which can explicitly account for temporal effects in the data stream monitored. A scoring system to detect and report alarms using these algorithms in a complementary way is proposed.<h4>Results</h4>The use of multiple algorithms in parallel resulted in increased system sensitivity. Specificity was decreased in simulated data, but the number of false alarms per year when the approach was applied to real data was considered manageable (between 1 and 3 per year for each of ten syndromic groups monitored). The automated implementation of this approach, including a method for on-line filtering of potential outbreak signals is described.<h4>Conclusion</h4>The developed system provides high sensitivity for detection of potential outbreak signals while also providing robustness and flexibility in establishing what signals constitute an alarm. This flexibility allows an analyst to customize the system for different syndromes.Fernanda C DóreaBeverly J McEwenW Bruce McNabJavier SanchezCrawford W ReviePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 12, p e82183 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Fernanda C Dórea Beverly J McEwen W Bruce McNab Javier Sanchez Crawford W Revie Syndromic surveillance using veterinary laboratory data: algorithm combination and customization of alerts.
description	<h4>Background</h4>Syndromic surveillance research has focused on two main themes: the search for data sources that can provide early disease detection; and the development of efficient algorithms that can detect potential outbreak signals.<h4>Methods</h4>This work combines three algorithms that have demonstrated solid performance in detecting simulated outbreak signals of varying shapes in time series of laboratory submissions counts. These are: the Shewhart control charts designed to detect sudden spikes in counts; the EWMA control charts developed to detect slow increasing outbreaks; and the Holt-Winters exponential smoothing, which can explicitly account for temporal effects in the data stream monitored. A scoring system to detect and report alarms using these algorithms in a complementary way is proposed.<h4>Results</h4>The use of multiple algorithms in parallel resulted in increased system sensitivity. Specificity was decreased in simulated data, but the number of false alarms per year when the approach was applied to real data was considered manageable (between 1 and 3 per year for each of ten syndromic groups monitored). The automated implementation of this approach, including a method for on-line filtering of potential outbreak signals is described.<h4>Conclusion</h4>The developed system provides high sensitivity for detection of potential outbreak signals while also providing robustness and flexibility in establishing what signals constitute an alarm. This flexibility allows an analyst to customize the system for different syndromes.
format	article
author	Fernanda C Dórea Beverly J McEwen W Bruce McNab Javier Sanchez Crawford W Revie
author_facet	Fernanda C Dórea Beverly J McEwen W Bruce McNab Javier Sanchez Crawford W Revie
author_sort	Fernanda C Dórea
title	Syndromic surveillance using veterinary laboratory data: algorithm combination and customization of alerts.
title_short	Syndromic surveillance using veterinary laboratory data: algorithm combination and customization of alerts.
title_full	Syndromic surveillance using veterinary laboratory data: algorithm combination and customization of alerts.
title_fullStr	Syndromic surveillance using veterinary laboratory data: algorithm combination and customization of alerts.
title_full_unstemmed	Syndromic surveillance using veterinary laboratory data: algorithm combination and customization of alerts.
title_sort	syndromic surveillance using veterinary laboratory data: algorithm combination and customization of alerts.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/a66fd7f0b81341a4b29a9e533aef586b
work_keys_str_mv	AT fernandacdorea syndromicsurveillanceusingveterinarylaboratorydataalgorithmcombinationandcustomizationofalerts AT beverlyjmcewen syndromicsurveillanceusingveterinarylaboratorydataalgorithmcombinationandcustomizationofalerts AT wbrucemcnab syndromicsurveillanceusingveterinarylaboratorydataalgorithmcombinationandcustomizationofalerts AT javiersanchez syndromicsurveillanceusingveterinarylaboratorydataalgorithmcombinationandcustomizationofalerts AT crawfordwrevie syndromicsurveillanceusingveterinarylaboratorydataalgorithmcombinationandcustomizationofalerts
_version_	1718421462521479168

Syndromic surveillance using veterinary laboratory data: algorithm combination and customization of alerts.

Ejemplares similares