An optimal surveillance system is one where the available sampling resources are allocated in time and space to best achieve detection objectives. This project aims to develop new methods for designing optimal surveillance systems that properly account for organism biology, trapping or sampling efficacy, and landscape characteristics.

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PBCRC 2110
Design and Evaluation of Targeted
Biosecurity Surveillance Systems
Michael Renton and Maggie Triska

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Problem being addressed
Optimal surveillance design
(what’s the best way to look for something you don’t want to find)

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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

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Problem being addressed
 What is the best design for a surveillance system?
- Number of samples (traps etc)
- Location of sampling
- Frequency of sampling

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General methods  specific applications
 Three case studies
- Grape phylloxera
- PCN
- Fruit fly

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Phylloxera

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Grape phylloxera spread model
Natural spread Natural + human + wind spread

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Grape phylloxera
High virulenceLow virulence
High suitability Medium suitability
Low suitability

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Grape phylloxera
 Standard
 ↑↓ Density
 Target high suitability soil

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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

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Victoria statistical areas
Properties
Movement
Market Seed
Network spread model

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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?

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Infested Detected
- Predict spread under
different surveillance
strategies
Spread simulations

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Likely paths of spread under different strategiesLikely paths of spread under different strategies

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Risk of Infestation

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Next Steps
• Detailed local spread
• Individual farm locations; roads; waterways; linked
properties

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Fruit fly

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Individual
trees
Orchards
High risk
introduction sites
Initial incursion

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Surveillance (trapping) designs
grid random

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adhockmeans
firstfirst … and I also got
the computer to
try to optimise…

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Results!
0 100 200 300 400
days to detection
probability
0.00010.0010.010.11
grid
adhoc
opt_time
opt_ninfs
firstfirst
kmeans
random
1 5 10 50 500 5000
N trees
probability
0.00010.0010.010.11
grid
adhoc
opt_time
opt_ninfs
firstfirst
kmeans
random Better!
N trees Days to detection
Probability
Probability

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Summary
 Packages for evaluating surveillance designs
- Account for biology, spread dynamics,
heterogeneous landscape
- Scales: field, farm, town, region, state
- Dispersal: active, passive, human

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Delivery
 What? (recommendations or tools?)
 Who? (us, end-users, others??)
 How?
- Training module for fruit fly
 Generalising to new
- locations, species, organisms, scales, situations

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Open questions and next steps
 Practicality, adoption, approval of designs?
 Sensitivity to
- biological assumptions?
- detection assumptions?
 Mobile traps and dynamic landscapes
 Economics

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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)

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Thanks!

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Grape phylloxera

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Output
• Detection vs infestation

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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