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Approaches to Targeting Biosecurity Risk
Adding Value to Valued Biosecurity
ABARES Outlook 2014 conference, 4-5 March 2014, Canberra
Andrew Robinson
CEBRA
The University of Melbourne
What we did as ACERA:
2006 – 2012
(Jane Elith, Michael Kearney,
John Leathwick)
Maxent
Age_class
AdultGrower
20.279.8
Herd type
BreederSlaughter
48.751.3
Herd status
InfectedUninfected
1.0099.0
Animal status
InfectedUninfected
.05099.9
Serology
PositiveNegative
.048 100
County
South JutlandOther county
9.1390.9
Sampling scheme
TargetedRepresentative
0 100
• Spatial analysis
• Expert judgement
• Disease freedom
• When to declare eradication
• Where should we search?
• Stakeholder mapping
• Consequences
• Effective inspection
• Intelligence-gathering software
(Tony Martin,
Greg Hood) (Cindy Hauser, Mick McCarthy,
Hugh Possingham, Tracy Rout,
Susie Hester, Oscar Cacho)
Bayes nets
Cost-effectiveness
analysis
Value of Biosecurity (Sonia Akter, Tom Kompas,
Michael Ward)
What We Are Doing:
2013 – 2017
CEBRA themes 1. Data Mining
2. Spatial Analysis
3. Biosecurity Intelligence
4. Benefit Cost Analysis
5. Pathway Analysis and Risk-Based Management
Regulator’s Conundrum
Biosecurity
Regulator
Protect us from Pests:
• Agriculture
• Industry
• Environment
but
Don’t:
• Cost too Much
• Take too Long
• Impede Trade
Regulator’s Conundrum
Biosecurity
Regulator
Protect us from Pests:
• Agriculture
• Industry
• Environment
but
Don’t:
• Cost too Much
• Take too Long
• Impede Trade
Let the Right One In
Value: Value for Effort
A: 00001001011000010010001000000100100001000101010000000000001
Oracle: Look where you should look
Value: Value for Effort
A: 00001001011000010010001000000100100001000101010000000000001
B: 00001000100001000000001000000100000000000001000000010000010
C: 00000000000000001000000000000000000000000000010000000000000
D: 00000000000000000000000000000000000000000000000000000000000
E: 00001001000010000000100000101000010101000000010001000000000
F: 00010010000000010000000100000000010000000000000010000000000
Oracle: Look where you should look
Letting the Right One In
When You Have History: Mine It
Data Mining with Border Compliance and ABARES
When You Have No History: Collect it.
CSP with Plant Import Operations and ABARES
… and build a Community of Practice
IBIS with NZ MPI
Using History Data mining
Lessons Learned:
1) You Can’t Find When You Don’t Hunt
2) Start with the Data You Have
3) Start Small – Use Case Studies
4) Keep your Eye on Operations
5) Build Bridges
Making History
Continuous Sampling Plan
For a given importer and tariff:
1) Inspect the next, say, 10 consignments.
If all clear, then start to monitor: Step 2.
If not, then inspect until 10 consecutive clears.
2) Monitor: inspect 1/5 randomly.
If all clear, then continue monitoring.
If not, then return to Step 1.
Monitor Inspect
All