Design and Evaluation of Targeted Biosecurity Surveillance Systems
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The document discusses the design and evaluation of targeted biosecurity surveillance systems for various agricultural pests, focusing on optimal strategies for detection and prevention. It presents case studies on grape phylloxera, potato cyst nematode (PCN), and fruit flies, exploring sampling methods, survey density, and effects on detection efficiency. Future work is proposed to improve practicality, generalization to different conditions, and detection methodologies.
Design and Evaluation of Targeted Biosecurity Surveillance Systems
- 1. biosecurity built on science PBCRC 2110 Design and Evaluation of Targeted Biosecurity Surveillance Systems Michael Renton and Maggie Triska Plant Biosecurity Cooperative Research Centre
- 2. biosecurity built on science Problem being addressed Optimal surveillance design (what’s the best way to look for something you don’t want to find)
- 3. biosecurity built on science Problem being addressed What is the best design for a surveillance system? - Number of samples (traps etc) - Location of sampling - Frequency of sampling
- 4. biosecurity built on science General methods specific applications Three case studies - Grape phylloxera - PCN - Fruit fly
- 5. biosecurity built on science Grape phylloxera
- 6. biosecurity built on science Grape phylloxera High virulenceLow virulence High suitability Medium suitability Low suitability
- 7. biosecurity built on science Grape phylloxera Standard ↑↓ Density Target high suitability soil
- 8. biosecurity built on science Grape phylloxera results
- 9. biosecurity built on science Grape phylloxera results Surveillance design based on soil types - More efficient Sampling density - Relatively minor effect Low virulent genotypes in low suitability conditions - Many, many years before detection
- 10. biosecurity built on science Vic statistical areas Properties Movement Fresh Seed PCN
- 11. biosecurity built on science Spread simulations Infested Detected - Predict spread under different surveillance strategies
- 12. biosecurity built on science PCN results
- 13. biosecurity built on science PCN summary Survey density ↑ - ↓ infested properties Survey arrangement (with fixed density) - variation between strategies Detection Surveillance: Region + Random
- 14. biosecurity built on science Fruit fly
- 15. biosecurity built on science Individual trees Orchards High risk introduction sites Initial Incursion Initial Incursion
- 16. biosecurity built on science Surveillance (trapping) designs grid random
- 17. biosecurity built on science adhockmeans firstfirst
- 18. biosecurity built on science Results! Better! 1 10 100 1000 10000 N trees probability 0.00010.0010.010.11 grid adhoc firstfirst kmeans random 0 100 200 300 400 days to detection probability 0.00010.0010.010.11 grid adhoc firstfirst kmeans random
- 19. biosecurity built on science NZ MPI: Q-fly case study Data from 2015 Q-fly incursion Analysis of surveillance to confirm eradication
- 20. biosecurity built on science Open questions and next steps Practicality and adoption of designs? Ease of use (training module for fruit fly) Ease of generalisation - to new locations, species, organisms, situations… Effects of biology and spread? Effects of better detection? - Better traps (sooner, longer distances, mobile, adaptive) - Better sampling/diagnostic methods
- 21. biosecurity built on science Thanks!
- 22. biosecurity built on science
- 23. biosecurity built on science
- 24. biosecurity built on science Grape phylloxera
- 25. biosecurity built on science PCN
- 26. biosecurity built on science
- 27. biosecurity built on science Probability of detection from active and passive surveillance increasing as a function of time since first infestation of a field. 0 5 10 15 0.00.20.40.60.81.0 t p active passive Detection and diagnostics? 1 5 10 50 500 5000 N trees probability 0.00010.0010.010.11 grid adhoc opt_time opt_ninfs firstfirst kmeans random