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

Research output: Contribution to conferenceAbstractpeer-review

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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.
Original languageEnglish
Publication statusPublished - 2016
Event2015 ISDS Conference - International Society for Disease Surveillance - Denver, United States
Duration: 8 Dec 2015 → …


Conference2015 ISDS Conference - International Society for Disease Surveillance
Country/TerritoryUnited States
Period8/12/15 → …


  • Syndromic Surveillance
  • Bayesian Networks
  • Structural Learning

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