Value of evidence from syndromic surveillance with cumulative evidence from multiple data streams with delayed reporting

Abstract Delayed reporting of health data may hamper the early detection of infectious diseases in surveillance systems. Furthermore, combining multiple data streams, e.g. aiming at improving a system’s sensitivity, can be challenging. In this study, we used a Bayesian framework where the result is...

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Autores principales:	R. Struchen, F. Vial, M. G. Andersson
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/cbfdeb05dae547cc95443a69ad85537c
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spelling	oai:doaj.org-article:cbfdeb05dae547cc95443a69ad85537c2021-12-02T16:08:24ZValue of evidence from syndromic surveillance with cumulative evidence from multiple data streams with delayed reporting10.1038/s41598-017-01259-52045-2322https://doaj.org/article/cbfdeb05dae547cc95443a69ad85537c2017-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-01259-5https://doaj.org/toc/2045-2322Abstract Delayed reporting of health data may hamper the early detection of infectious diseases in surveillance systems. Furthermore, combining multiple data streams, e.g. aiming at improving a system’s sensitivity, can be challenging. In this study, we used a Bayesian framework where the result is presented as the value of evidence, i.e. the likelihood ratio for the evidence under outbreak versus baseline conditions. Based on a historical data set of routinely collected cattle mortality events, we evaluated outbreak detection performance (sensitivity, time to detection, in-control run length) under the Bayesian approach among three scenarios: presence of delayed data reporting, but not accounting for it; presence of delayed data reporting accounted for; and absence of delayed data reporting (i.e. an ideal system). Performance on larger and smaller outbreaks was compared with a classical approach, considering syndromes separately or combined. We found that the Bayesian approach performed better than the classical approach, especially for the smaller outbreaks. Furthermore, the Bayesian approach performed similarly well in the scenario where delayed reporting was accounted for to the scenario where it was absent. We argue that the value of evidence framework may be suitable for surveillance systems with multiple syndromes and delayed reporting of data.R. StruchenF. VialM. G. AnderssonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q R. Struchen F. Vial M. G. Andersson Value of evidence from syndromic surveillance with cumulative evidence from multiple data streams with delayed reporting
description	Abstract Delayed reporting of health data may hamper the early detection of infectious diseases in surveillance systems. Furthermore, combining multiple data streams, e.g. aiming at improving a system’s sensitivity, can be challenging. In this study, we used a Bayesian framework where the result is presented as the value of evidence, i.e. the likelihood ratio for the evidence under outbreak versus baseline conditions. Based on a historical data set of routinely collected cattle mortality events, we evaluated outbreak detection performance (sensitivity, time to detection, in-control run length) under the Bayesian approach among three scenarios: presence of delayed data reporting, but not accounting for it; presence of delayed data reporting accounted for; and absence of delayed data reporting (i.e. an ideal system). Performance on larger and smaller outbreaks was compared with a classical approach, considering syndromes separately or combined. We found that the Bayesian approach performed better than the classical approach, especially for the smaller outbreaks. Furthermore, the Bayesian approach performed similarly well in the scenario where delayed reporting was accounted for to the scenario where it was absent. We argue that the value of evidence framework may be suitable for surveillance systems with multiple syndromes and delayed reporting of data.
format	article
author	R. Struchen F. Vial M. G. Andersson
author_facet	R. Struchen F. Vial M. G. Andersson
author_sort	R. Struchen
title	Value of evidence from syndromic surveillance with cumulative evidence from multiple data streams with delayed reporting
title_short	Value of evidence from syndromic surveillance with cumulative evidence from multiple data streams with delayed reporting
title_full	Value of evidence from syndromic surveillance with cumulative evidence from multiple data streams with delayed reporting
title_fullStr	Value of evidence from syndromic surveillance with cumulative evidence from multiple data streams with delayed reporting
title_full_unstemmed	Value of evidence from syndromic surveillance with cumulative evidence from multiple data streams with delayed reporting
title_sort	value of evidence from syndromic surveillance with cumulative evidence from multiple data streams with delayed reporting
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/cbfdeb05dae547cc95443a69ad85537c
work_keys_str_mv	AT rstruchen valueofevidencefromsyndromicsurveillancewithcumulativeevidencefrommultipledatastreamswithdelayedreporting AT fvial valueofevidencefromsyndromicsurveillancewithcumulativeevidencefrommultipledatastreamswithdelayedreporting AT mgandersson valueofevidencefromsyndromicsurveillancewithcumulativeevidencefrommultipledatastreamswithdelayedreporting
_version_	1718384496564240384

Value of evidence from syndromic surveillance with cumulative evidence from multiple data streams with delayed reporting

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