Surveillance systems are an essential component of biosecurity. Design of biosecurity surveillance systems may include designs of grids of static traps, plans for field sampling, or deployment of potentially "game-changing" mobile trap technology. The aim of these systems is to achieve defined detection objectives, (e.g. early detection, supporting area-freedom status) at minimum cost. This project will develop and apply statistically-based surveillance systems that account for organism biology, trap behaviour and landscape characteristics.

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PBCRC 2110
Design and Evaluation of Targeted
Biosecurity Surveillance Systems
Michael Renton and Maggie Triska
Plant Biosecurity Cooperative Research Centre

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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
 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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Grape phylloxera

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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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Grape phylloxera results

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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

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Vic statistical areas
Properties
Movement
Fresh Seed
PCN

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Spread simulations
Infested Detected
- Predict spread under
different surveillance
strategies

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PCN results

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PCN summary
 Survey density ↑
- ↓ infested properties
 Survey arrangement
(with fixed density)
- variation between
strategies
Detection
Surveillance: Region +
Random

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Fruit fly

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Individual
trees
Orchards
High risk
introduction
sites
Initial
Incursion
Initial
Incursion

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Surveillance (trapping) designs
grid random

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adhockmeans
firstfirst

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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

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NZ MPI: Q-fly case study
 Data from 2015 Q-fly
incursion
 Analysis of surveillance
to confirm eradication

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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

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Thanks!

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Grape phylloxera

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PCN

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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