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

CEBRA: Who We Are

UCRIVERSIDE

Mark Burgman

Andrew Robinson

Tom Kompas

Susie Hester

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

Building Bridges

Overview

The Regulator’s Life is Not a Happy One

CEBRA & ABARES Provide Innovation and Support

http://www.cebra.unimelb.edu.au