Using Bayesian networks to assist decision-making in syndromic surveillance

Colón-González, Felipe J., Lake, Iain, 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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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 (, 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
Faculty \ School: Faculty of Science > School of Environmental Sciences
Faculty of Science > Tyndall Centre for Climatic Change
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Depositing User: Pure Connector
Date Deposited: 15 Nov 2016 10:00
Last Modified: 24 Nov 2020 01:50
DOI: 10.5210/ojphi.v8i1.6415

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