Using Bayesian networks to model surveillance in complex plant and animal health systems

Johnson, Sandra, Mengersen, Kerrie, Ormsby, Michael, & Whittle, Peter (2015) Using Bayesian networks to model surveillance in complex plant and animal health systems. In Jarrad, Frith, Low-Choy, Samantha, & Mengersen, Kerrie (Eds.) Biosecurity Surveillance - Quantitative Approaches. CABI, Wallingford, Oxenford, United Kingdom, pp. 278-295.

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In this chapter we consider biosecurity surveillance as part of a complex system comprising many different biological, environmental and human factors and their interactions. Modelling and analysis of surveillance strategies should take into account these complexities, and also facilitate the use and integration of the many types of different information that can provide insight into the system as a whole. After a brief discussion of a range of options, we focus on Bayesian networks for representing such complex systems. We summarize the features of Bayesian networks and describe these in the context of surveillance.

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ID Code: 94424
Item Type: Book Chapter
Keywords: animals, monitoring, insects, models, plant pests, surveillance, pest insects, Bayesian theory
DOI: 10.1079/9781780643595.0000
ISBN: 9781780643595
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)
Australian and New Zealand Standard Research Classification > ENVIRONMENTAL SCIENCES (050000) > ENVIRONMENTAL SCIENCE AND MANAGEMENT (050200) > Environmental Monitoring (050206)
Divisions: Current > Research Centres > ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS)
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > Institutes > Institute for Future Environments
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 CABI
Deposited On: 07 Apr 2016 05:54
Last Modified: 18 Apr 2016 00:09

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