Using Bayesian Networks to Assist Decision-Making in Syndromic Surveillance
PDF

How to Cite

Colón-González F. J., Lake, I., Barker, G., Smith, G. E., Elliot, A. J., & Morbey, R. (2016). Using Bayesian Networks to Assist Decision-Making in Syndromic Surveillance. Online Journal of Public Health Informatics, 8(1). https://doi.org/10.5210/ojphi.v8i1.6415

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.

https://doi.org/10.5210/ojphi.v8i1.6415
PDF
Authors own copyright of their articles appearing in the Online Journal of Public Health Informatics. Readers may copy articles without permission of the copyright owner(s), as long as the author and OJPHI are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. Share-alike: when posting copies or adaptations of the work, release the work under the same license as the original. For any other use of articles, please contact the copyright owner. The journal/publisher is not responsible for subsequent uses of the work, including uses infringing the above license. It is the author's responsibility to bring an infringement action if so desired by the author.