Using Bayesian Networks to Assist Decision-Making in Syndromic Surveillance


  • Felipe J. Colón-González
  • Iain Lake
  • Gary Barker
  • Gillian E. Smith
  • Alex J. Elliot
  • Roger Morbey



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.




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).



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