What do we learn from general surveillance

How well can

General Surveillance reveal exotic pests?

(There’s a cow in the room.)

Presentation for Science Exchange, 24 May 2012

Samantha Low Choy

Cooperative Research Centre for National Plant Biosecurity @ Mathematical Sciences, Science & Engineering Faculty QUT

Jo Slattery

Plant Health Australia

Sharyn Taylor

Plant Health Australia

Matt Falk

Math Sciences, QUT

WA Surveillance Experts DAFWA

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THE PROBLEM biosecurity built on science

Surveillance

Looking for a needle in a haystack

 Seems like an impossible task!

- But how does our thinking frame the task? biosecurity built on science

Surveillance

Looking for a needle in a haystack

 Random sample of patches, then randomly select a plant within a patch biosecurity built on science

Surveillance

Looking for a needle in a haystack

- Transect/blob sampling: jump then look - Path/zigzag sampling: traverse the area biosecurity built on science

Surveillance

What are we looking at?  The unit of surveillance is a plant?

biosecurity built on science

Surveillance

What are we looking at?  The unit of surveillance is a cow, n ~ laboratory testing

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Surveillance

What are we looking at?  The unit of general surveillance is what the farmer can see during general farm activities

biosecurity built on science

Surveillance

What are we looking at?

 For broadacre crop, the unit of general surveillance comprises many plants - We need to describe how a pest might be revealed by what farmers generally do biosecurity built on science

Surveillance

What are we looking at?

 For broadacre crop, search & detection depends on scale - We need to describe how a pest might be revealed by what farmers generally do biosecurity built on science

WANTED

High priority pest Significant crop loss, wheat and barley ↑complexity of management ↑research for breeding & chemical management

RUSSIAN WHEAT APHID SUNN PEST, HESSIAN FLY

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Avenues for detection

 Two-phase sampling but not @ plant scale

(1) Scan from vehicle -

suspicious patch in visible patches

(2) Trigger closer inspection -

suspicious plants target worst symptoms trigger report?

 Incidental sampling biosecurity built on science

0

1

Skill / pass

High

Low

2. Close Inspection

0.00

0.25

0.50

0.75

1.00 0

Skill / pass

FPR

0.25

0.5

0.75

1

I. Vehicle Scan High

Moderate

Detectability @ patch

Low

Life stage of aphid

of worst symptoms consistent with pest

Detectability @ plant

0.75

Moderate

@ paddock, patch, plant

of worst symptoms consistent with pest

0.5

TPR I. Vehicle Scan

Elicited Detectability

Detectability @ paddock

0.25

2. Close Inspection

0.00

0.25

0.50

0.75

1.00

0

0.25

0.5

0.75

1

3. TPR Nymphs

Wingless adults

of the pest 3. Laboratory

3. FPR 0.00

0.25

0.50

0.75

1.00

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Zero predictive value

A role for FPs (TNs) and FNs (TPs)  What does it mean when you find nothing?

Could the pest still be present?

- Evaluate the zero predictive value (aka NPV)  Fielding & Bell (1997) Tony Martin et al (2007)

- Use Bayes’ Theorem  Hilborn & Mangel (1993) Bayes (1786)

 Usual emphasis is on sensitivity of surveillance  Easier to compute!

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Zero predictive value A role for FPs and 0s

 What does it mean when you find nothing?

Could the pest still be present?

  Missed when present, at LMH levels ZPV = 1 1 + odds No false alarms when absent  

  

TNR(Absent) Weighted by a priori risk of absence (quantify Pest Risk Assessment)

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Zero predictive value A role for FPs and 0s

 What does it mean when you find nothing?

Could the pest still be present?

  Missed when present, at LMH levels  ZPV = 1 1 + odds  No false alarms when absent    FNR if present @ Low levels + FNR if present @ Medium levels + FNR if present @ High levels

Weighted by a priori risk of prevalence at each level (Each level is not equally likely)

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Learning by doing

expert estimates of risk, detectability, biology

 The Bayesian cycle of learning

40

- Supports science via multiple working hypotheses, not fixing on single null hypothesis

10

20

30

EPSS = 3 : Pr(p<.01)= 0.019 , and 95% chance p no bigger than 0.780 EPSS = 10 : Pr(p<.01)= 0.068 , and 95% chance p no bigger than 0.300 EPSS = 30 : Pr(p<.01)= 0.150 , and 95% chance p no bigger than 0.110 EPSS = 100 : Pr(p<.01)= 0.270 , and 95% chance p no bigger than 0.047

0

Plausibility

Elicited p: best estimate = 1%

0.00

0.02

0.04

0.06

0.08

0.10

Probability pest present,biosecurity p built on science

ZPV as %Missed infested plants MIP=0 means area freedom

0.8

Visibility (depth of road) 2% 10% 100%

0.4

1 in 5 chance that MIP under 50%

0.2

0.6

Low plausibility (2.4%) that MIP under 10.

1 in 4 chance of area freedom.

0.0

Cumulative plausibility

1.0

Hardly any chance (1 in 1000) of area freedom.

0

10

20

30

40

50

Percentage of missed infested plants (%)

High plausibility (99%) that MIP under 10%. biosecurity built on science

MODELLING ISSUES biosecurity built on science

Benefit of repeating 0.2

Myrtle Rust

Low-Choy+2011

0.00

0.05

Plausibility 0.10 0.15

Time 1 Time 2

0

5

10

15

20

25

30

After 4 weeks, typical scenario 40 blocks searched • the mean infested #plants doubles (5.97→12.08) •95% sure infested #plants >doubles (17→46) Can harness Bayesian cycle of learning to adapt as information gained & knowledge refined. biosecurity built on science

Complex detectability

Extrapolated a curve

1.0 0.8 0.6 0.4

Elicited three quantiles (like a bioassay curve)

Pr(Inspect| Damage=10%)=0.049 Pr(Inspect| Damage=25%)=0.572 Pr(Inspect| Damage=50%)=0.980 Pr(Inspect| Damage=75%)=0.999 Pr(Inspect| Damage=90%)=1.000 Pr(Inspect| Damage=95%)=1.000 Elicited

0.2

Triggering a close inspection depends on level of damage

Pr(Inspect | Damage=33%)=0.70 Pr(Inspect | Damage=33%)=0.80 Pr(Inspect | Damage=33%)=0.95

0.0

Sunn pest

Probability of follow-up with close inspe

Beyond a single point estimate

0.0

0.2

0.4

0.6 Level of damage

0.8

1.0

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Putting it all together

A hierarchical model for assimilated biosecurity in action

Detect browning (T/FP) Presence of the pest

Phase 1: vehicle scan

Missed browning (FN)

Trigger Phase 2: Close-up Phase 2 not triggered

Detect, with browning (T/FP) Missed, with browning (T/FN)

Confirmed (T/FP) Report Rejected (T/FN)

Confirmed no browning (TN)

WHY BOTHER (cf simple 2-stage design)?

If you donâ&#x20AC;&#x2122;t want to assume: perfect detection & reporting and a single method of detection, plant scale of detectability, ignore FPR & risk of prevalence â&#x2021;&#x2019; focus on sensitivity not ZPV, random sampling biosecurity built on science

Zeros for detecting pests/disease/resistance • Hu, W., O’Leary, R. A., Mengersen, K., and Low-Choy, S. (to appear). Bayesian classification and regression trees for predicting incidence of cryptosporidiosis, PLoS ONE. • Falk, M., Low Choy, S., Collins, P., Nayak, M. (in prep). Bayesian hurdle models for identifying factors that affect incidence and trends in pest resistance.

Designing surveillance – the conceptual model for model-based sampling design  Anderson, C., Low Choy, S., Dominiak, B., Gillespie, P. S.; Davis, R., Gambley, C., Loecker, H., Pheloung, P., Smith, L., Taylor, S.; Whittle, P. (accepted 29 June 2011) “Biosecurity Surveillance Systems, Plants”, In McKirdy, S. (ed), Biosecurity in Agriculture and the Environment, CABI.  Low-Choy, S., Daglish, G., Ridley, A., Burrill, P. (submitted) “Bayesian adjustment of sampling biases for small intensive surveys on farm management practices relevant to biosecurity”  Low-Choy, S., Hammond, N., Penrose, L., Anderson, C., and Taylor, S. (2011b). In Chan et al (eds) Proceedings MODSIM 2011, www.mssanz.org.au/modsim2011/E16/low_choy.pdf  Low-Choy, S., Taylor, S., et al(in prep) “Evaluating general surveillance for early detection of exemplar exotic plant pests”  Low-Choy, S., Whittle, P., and Anderson, C. (accepted 29 June 2011). Quantitative approaches to designing plant biosecurity surveillance, In McKirdy, S. (ed), Biosecurity in Agriculture and the Environment, CABI. Elicitation • Albert, I., Donnet, S., Guihenneuc, C., Low Choy, S., Mengersen, K., and Rousseau, J. (to appear). Combining expert opinions in prior elicitation, Bayesian Analysis.

• Johnson, S., Low-Choy, S. and Mengersen, K. (to appear) “Integrating Bayesian networks and Geographic information systems”, Integ Environ Assess Mgmt. onlinelibrary.wiley.com/doi/10.1002/ieam.262/pdf.

• Low Choy, S., Murray, J., James, A. and Mengersen, K. (2010) “Indirect elicitation from ecological experts: from methods and software to habitat modelling and rock-wallabies” in O’Hagan, A. and West, M., (eds) The Oxford Handbook of Applied Bayesian Analysis, Oxford University Press: UK, pp 511-544.

• O’Leary, R., Fisher, R., Low Choy, S., Mengersen, K., Caley, M. J. (2011) What is an expert? In Chan, F. et al (eds) Proceedings MODSIM2011, www.mssanz.org.au/modsim2011/e9/oleary.pdf  Martin, T. G., Burgman, M. A., Fidler, F., Kuhnert, P. M., Low-Choy, S., McBride, M., Mengersen, K. (2012) Eliciting Expert Knowledge in Conservation Science, Conservation Biology, 26(1): 29-38.  Low-Choy, S. (in press). Priors: Silent or active partners in Bayesian inference? In Alston, C. et al (eds) Case Studies in Bayesian Statistical Modelling and Analysis, John Wiley & Sons, Inc: London.  Fisher, R., O’Leary, R., Low-Choy, S., Mengersen, K., and Caley, J. (to appear). Elicit-n: New method and software for eliciting species richness, Environmental Modelling & Software.

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Acknowledgements The experts

        

John Botha Cameron Brumley Rob Emery Darryl Hardie Alan Lord Marc Poole Jeff Russell Dusty Severtson Andy Szito biosecurity built on science