design and evaluation of targeted biosecurity surveillance systems
TRANSCRIPT
biosecurity built on science
PBCRC 2110 Design and Evaluation of Targeted Biosecurity Surveillance Systems
Michael Renton and Maggie Triska
Plant Biosecurity Cooperative Research Centre
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
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
Grape phylloxera
biosecurity built on science
Grape phylloxera
High virulenceLow virulence
High suitabilityMedium suitability
Low suitability
biosecurity built on science
Grape phylloxera
Standard
↑↓ Density
Target high
suitability soil
biosecurity built on science
Grape phylloxera results
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
biosecurity built on science
Vic statistical areas
Properties
Movement
Fresh Seed
PCN
biosecurity built on science
Spread simulations
Infested Detected
- Predict spread under different surveillance strategies
biosecurity built on science
PCN results
biosecurity built on science
PCN summary
Survey density ↑
- ↓ infested properties
Survey arrangement (with fixed density)
- variation between strategies
Detection
Surveillance: Region +
Random
biosecurity built on science
Fruit fly
biosecurity built on science
Individual
trees
Orchards
High risk
introduction
sites
Initial
Incursion
Initial
Incursion
biosecurity built on science
Surveillance (trapping) designs
grid random
biosecurity built on science
adhockmeans
firstfirst
biosecurity built on science
Results!
Better!
1 10 100 1000 10000
N trees
pro
ba
bility
0.0
00
10
.00
10
.01
0.1
1
gridadhoc
firstfirstkmeansrandom
0 100 200 300 400
days to detection
pro
ba
bility
0.0
00
10
.00
10
.01
0.1
1
gridadhoc
firstfirstkmeansrandom
biosecurity built on science
NZ MPI: Q-fly case study
Data from 2015 Q-fly incursion
Analysis of surveillance to confirm eradication
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
biosecurity built on science
Thanks!
biosecurity built on science
biosecurity built on science
biosecurity built on science
Grape phylloxera
biosecurity built on science
PCN
biosecurity built on science
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
active
passive
Detection and diagnostics?
1 5 10 50 500 5000
N trees
pro
ba
bility
0.0
00
10
.00
10
.01
0.1
1grid
adhocopt_timeopt_ninfs
firstfirstkmeansrandom