Advancing a framework to enable characterization and evaluation of data streams useful for biosurveillance.

In recent years, biosurveillance has become the buzzword under which a diverse set of ideas and activities regarding detecting and mitigating biological threats are incorporated depending on context and perspective. Increasingly, biosurveillance practice has become global and interdisciplinary, requ...

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Main Authors: Kristen J Margevicius, Nicholas Generous, Kirsten J Taylor-McCabe, Mac Brown, W Brent Daniel, Lauren Castro, Andrea Hengartner, Alina Deshpande
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2014-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC3879288?pdf=render
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author Kristen J Margevicius
Nicholas Generous
Kirsten J Taylor-McCabe
Mac Brown
W Brent Daniel
Lauren Castro
Andrea Hengartner
Alina Deshpande
author_facet Kristen J Margevicius
Nicholas Generous
Kirsten J Taylor-McCabe
Mac Brown
W Brent Daniel
Lauren Castro
Andrea Hengartner
Alina Deshpande
author_sort Kristen J Margevicius
collection DOAJ
description In recent years, biosurveillance has become the buzzword under which a diverse set of ideas and activities regarding detecting and mitigating biological threats are incorporated depending on context and perspective. Increasingly, biosurveillance practice has become global and interdisciplinary, requiring information and resources across public health, One Health, and biothreat domains. Even within the scope of infectious disease surveillance, multiple systems, data sources, and tools are used with varying and often unknown effectiveness. Evaluating the impact and utility of state-of-the-art biosurveillance is, in part, confounded by the complexity of the systems and the information derived from them. We present a novel approach conceptualizing biosurveillance from the perspective of the fundamental data streams that have been or could be used for biosurveillance and to systematically structure a framework that can be universally applicable for use in evaluating and understanding a wide range of biosurveillance activities. Moreover, the Biosurveillance Data Stream Framework and associated definitions are proposed as a starting point to facilitate the development of a standardized lexicon for biosurveillance and characterization of currently used and newly emerging data streams. Criteria for building the data stream framework were developed from an examination of the literature, analysis of information on operational infectious disease biosurveillance systems, and consultation with experts in the area of biosurveillance. To demonstrate utility, the framework and definitions were used as the basis for a schema of a relational database for biosurveillance resources and in the development and use of a decision support tool for data stream evaluation.
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spelling doaj.art-e736e212b8aa499fb1ae4992b87eea7d2022-12-21T22:39:44ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0191e8373010.1371/journal.pone.0083730Advancing a framework to enable characterization and evaluation of data streams useful for biosurveillance.Kristen J MargeviciusNicholas GenerousKirsten J Taylor-McCabeMac BrownW Brent DanielLauren CastroAndrea HengartnerAlina DeshpandeIn recent years, biosurveillance has become the buzzword under which a diverse set of ideas and activities regarding detecting and mitigating biological threats are incorporated depending on context and perspective. Increasingly, biosurveillance practice has become global and interdisciplinary, requiring information and resources across public health, One Health, and biothreat domains. Even within the scope of infectious disease surveillance, multiple systems, data sources, and tools are used with varying and often unknown effectiveness. Evaluating the impact and utility of state-of-the-art biosurveillance is, in part, confounded by the complexity of the systems and the information derived from them. We present a novel approach conceptualizing biosurveillance from the perspective of the fundamental data streams that have been or could be used for biosurveillance and to systematically structure a framework that can be universally applicable for use in evaluating and understanding a wide range of biosurveillance activities. Moreover, the Biosurveillance Data Stream Framework and associated definitions are proposed as a starting point to facilitate the development of a standardized lexicon for biosurveillance and characterization of currently used and newly emerging data streams. Criteria for building the data stream framework were developed from an examination of the literature, analysis of information on operational infectious disease biosurveillance systems, and consultation with experts in the area of biosurveillance. To demonstrate utility, the framework and definitions were used as the basis for a schema of a relational database for biosurveillance resources and in the development and use of a decision support tool for data stream evaluation.http://europepmc.org/articles/PMC3879288?pdf=render
spellingShingle Kristen J Margevicius
Nicholas Generous
Kirsten J Taylor-McCabe
Mac Brown
W Brent Daniel
Lauren Castro
Andrea Hengartner
Alina Deshpande
Advancing a framework to enable characterization and evaluation of data streams useful for biosurveillance.
PLoS ONE
title Advancing a framework to enable characterization and evaluation of data streams useful for biosurveillance.
title_full Advancing a framework to enable characterization and evaluation of data streams useful for biosurveillance.
title_fullStr Advancing a framework to enable characterization and evaluation of data streams useful for biosurveillance.
title_full_unstemmed Advancing a framework to enable characterization and evaluation of data streams useful for biosurveillance.
title_short Advancing a framework to enable characterization and evaluation of data streams useful for biosurveillance.
title_sort advancing a framework to enable characterization and evaluation of data streams useful for biosurveillance
url http://europepmc.org/articles/PMC3879288?pdf=render
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