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biosecurity built on science
PBCRC 2110 Design and Evaluation of Targeted Biosecurity Surveillance Systems
Michael Renton and Maggie Triska
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)
biosecurity built on science
Problem being addressed
Better surveillance- Early detection for rapid and effective response
- Delineating the extent of an incursion- Proving area freedom to protect trade
- Inform management of established pests
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
biosecurity built on science
General methods specific applications
Three case studies- Grape phylloxera
- PCN
- Fruit fly
biosecurity built on science
Phylloxera
biosecurity built on science
Grape phylloxera spread model
Natural spread Natural + human + wind spread
biosecurity built on science
Grape phylloxera
High virulenceLow virulence
High suitability Medium suitability
Low suitability
biosecurity built on science
Grape phylloxera
Standard ↑↓ Density Target high suitability soil
biosecurity built on science
Phylloxera
Surveillance design based on soil types - More efficient
Sampling density- Relatively minor effect
Low virulence in low suitability conditions- Many, many years before detection
biosecurity built on science
Victoria statistical areasProperties
Movement
Market Seed
Network spread model
biosecurity built on science
Surveillance strategies
- Number of properties?- Fixed or vary with time?- Random across space?- Focus on areas with
detections? - Plus neighbouring areas?- More connected nodes?- Weighted strategies?
biosecurity built on science
Infested Detected
- Predict spread under different surveillance strategies
Spread simulations
biosecurity built on science
Likely paths of spread under different strategiesLikely paths of spread under different strategies
biosecurity built on science
Risk of Infestation
biosecurity built on science
Next Steps
• Detailed local spread• Individual farm locations; roads; waterways; linked
properties
biosecurity built on science
Fruit fly
biosecurity built on science
Individual trees
Orchards
High risk introduction sites
Initial incursion
biosecurity built on science
Surveillance (trapping) designs
grid random
biosecurity built on science
adhockmeans
firstfirst … and I also got the computer to try to optimise…
biosecurity built on science
Results!
0 100 200 300 400
days to detection
prob
abili
ty
0.00
010.
001
0.01
0.1
1
gridadhocopt_timeopt_ninfsfirstfirstkmeansrandom
1 5 10 50 500 5000
N trees
prob
abili
ty
0.00
010.
001
0.01
0.1
1
gridadhocopt_timeopt_ninfsfirstfirstkmeansrandom Better!
N trees Days to detection
Prob
abili
ty
Prob
abili
ty
biosecurity built on science
Summary
Packages for evaluating surveillance designs - Account for biology, spread dynamics,
heterogeneous landscape- Scales: field, farm, town, region, state- Dispersal: active, passive, human
biosecurity built on science
Delivery
What? (recommendations or tools?) Who? (us, end-users, others??) How?
- Training module for fruit fly Generalising to new
- locations, species, organisms, scales, situations
biosecurity built on science
Open questions and next steps
Practicality, adoption, approval of designs? Sensitivity to
- biological assumptions?- detection assumptions?
Mobile traps and dynamic landscapes Economics
biosecurity built on science
End User’s Perspective
“Project outcomes are expected to assist in the development of surveillance systems which achieve the required outcomes at the least cost. ” Bonny Vogelzang (PIRSA)
biosecurity built on science
Thanks!
biosecurity built on science
biosecurity built on science
Grape phylloxera
biosecurity built on science
biosecurity built on science
Output
• Detection vs infestation
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
0.0
0.2
0.4
0.6
0.8
1.0
t
p
activepassive
Detection and diagnostics?
1 5 10 50 500 5000
N trees
prob
abili
ty
0.00
010.
001
0.01
0.1
1
gridadhocopt_timeopt_ninfsfirstfirstkmeansrandom