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.
| Original language | English |
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| DOIs | |
| Publication status | Published - 2016 |
| Event | 2015 ISDS Conference - International Society for Disease Surveillance - Denver, United States Duration: 8 Dec 2015 → … |
Conference
| Conference | 2015 ISDS Conference - International Society for Disease Surveillance |
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| Country/Territory | United States |
| City | Denver |
| Period | 8/12/15 → … |
Keywords
- Syndromic Surveillance
- Bayesian Networks
- Structural Learning
Access to Document
- Published manuscript
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.