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Colón-González, Felipe J., Lake, Iain 
ORCID: https://orcid.org/0000-0003-4407-5357, Barker, Gary, Smith, Gillian E., Elliot, Alex J. and Morbey, Roger
(2016)
Using Bayesian networks to assist decision-making in syndromic surveillance.
In: 2015 ISDS Conference - International Society for Disease Surveillance, 2015-12-08.
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Abstract
The decision as to whether an alarm (excess activity in syndromic surveillance indicators) leads to an alert (a public health response) is often based on expert knowledge. Expert-based approaches may produce faster results than automated approaches but could be difficult to replicate. Moreover, the effectiveness of a syndromic surveillance system could be compromised in the absence of such experts. Bayesian network structural learning provides a mechanism to identify and represent relations between syndromic indicators, and between these indicators and alerts. Their outputs have the potential to assist decision-makers determine more effectively which alarms are most likely to lead to alerts.
| Item Type: | Conference or Workshop Item (Other) |
|---|---|
| Additional Information: | ISDS Annual Conference Proceedings 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Uncontrolled Keywords: | syndromic surveillance,bayesian networks,structural learning,sdg 3 - good health and well-being ,/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being |
| Faculty \ School: | Faculty of Science > School of Environmental Sciences |
| UEA Research Groups: | University of East Anglia Schools > Faculty of Science > Tyndall Centre for Climate Change Research Faculty of Science > Research Centres > Tyndall Centre for Climate Change Research Faculty of Science > Research Groups > Environmental Social Sciences |
| Related URLs: | |
| Depositing User: | Pure Connector |
| Date Deposited: | 15 Nov 2016 10:00 |
| Last Modified: | 31 Jul 2023 00:20 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/61332 |
| DOI: | 10.5210/ojphi.v8i1.6415 |
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