Session 6: Design and Evaluation of Targeted Biosecurity Surveillance Systems

biosecurity built on science

PBCRC 2110 Design and Evaluation of Targeted Biosecurity Surveillance Systems

Michael Renton and Maggie Triska


Problem being addressed

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



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



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


General methods specific applications

Three case studies- Grape phylloxera

- PCN

- Fruit fly


Phylloxera


Grape phylloxera spread model

Natural spread Natural + human + wind spread


Grape phylloxera

High virulenceLow virulence

High suitability Medium suitability

Low suitability


Grape phylloxera

Standard ↑↓ Density Target high suitability soil


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


Victoria statistical areasProperties

Movement

Market Seed

Network spread model


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?


Infested Detected

- Predict spread under different surveillance strategies

Spread simulations


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


Risk of Infestation


Next Steps

• Detailed local spread• Individual farm locations; roads; waterways; linked

properties


Fruit fly


Individual trees

Orchards

High risk introduction sites

Initial incursion


Surveillance (trapping) designs

grid random


adhockmeans

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


Results!

0 100 200 300 400

days to detection

prob

abili

ty

0.00

010.

001

0.01

0.1

1

gridadhocopt_timeopt_ninfsfirstfirstkmeansrandom

1 5 10 50 500 5000

N trees

prob

abili

ty

0.00

010.

001

0.01

0.1

1

gridadhocopt_timeopt_ninfsfirstfirstkmeansrandom Better!

N trees Days to detection

Prob

abili

ty

Prob

abili

ty


Summary

Packages for evaluating surveillance designs - Account for biology, spread dynamics,

heterogeneous landscape- Scales: field, farm, town, region, state- Dispersal: active, passive, human


Delivery

What? (recommendations or tools?) Who? (us, end-users, others??) How?

- Training module for fruit fly Generalising to new

- locations, species, organisms, scales, situations


Open questions and next steps

Practicality, adoption, approval of designs? Sensitivity to

- biological assumptions?- detection assumptions?

Mobile traps and dynamic landscapes Economics


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)


Thanks!



Grape phylloxera



Output

• Detection vs infestation


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

activepassive

Detection and diagnostics?

1 5 10 50 500 5000

N trees

prob

abili

ty

0.00

010.

001

0.01

0.1

1

gridadhocopt_timeopt_ninfsfirstfirstkmeansrandom

Science

Session 6: Design and Evaluation of Targeted Biosecurity Surveillance Systems