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