Bayesian modelling of surveillance and proof of freedom : the mathematical, logical & psychological challenges

Low-Choy, Samantha (2013) Bayesian modelling of surveillance and proof of freedom : the mathematical, logical & psychological challenges. In Anderssen, Bob (Ed.) A Planet at Risk - Bioinvasion & Biosecurity Workshop, 12-13 September 2013, CSIRO Discovery Centre, Canberra, Australia. (Unpublished)

Abstract

Keeping exotic plant pests out of our country relies on good border control or quarantine. However with increasing globalization and mobilization some things slip through. Then the back up systems become important. This can include an expensive form of surveillance that purposively targets particular pests. A much wider net is provided by general surveillance, which is assimilated into everyday activities, like farmers checking the health of their crops. In fact farmers and even home gardeners have provided a front line warning system for some pests (eg European wasp) that could otherwise have wreaked havoc.

Mathematics is used to model how surveillance works in various situations. Within this virtual world we can play with various surveillance and management strategies to "see" how they would work, or how to make them work better. One of our greatest challenges is estimating some of the input parameters : because the pest hasn't been here before, it's hard to predict how well it might behave: establishing, spreading, and what types of symptoms it might express. So we rely on experts to help us with this. This talk will look at the mathematical, psychological and logical challenges of helping experts to quantify what they think. We show how the subjective Bayesian approach is useful for capturing expert uncertainty, ultimately providing a more complete picture of what they think... And what they don't!

Impact and interest:

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ID Code: 62616
Item Type: Conference Item (Presentation)
Refereed: No
Additional URLs:
Keywords: Plant pests, Bayesian statistical modelling, Surveillance, Performance, Negative Predictive Value
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)
Australian and New Zealand Standard Research Classification > AGRICULTURAL AND VETERINARY SCIENCES (070000) > AGRICULTURE LAND AND FARM MANAGEMENT (070100) > Sustainable Agricultural Development (070108)
Divisions: Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 Samantha Low-Choy
Deposited On: 17 Sep 2013 23:23
Last Modified: 17 Sep 2013 23:24

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