Ships arriving in Australia may have visited multiple ports along the way. These complex pathways present opportunities for pest species, such as the Asian Gypsy Moth, to arrive into Australia from indirect routes. Understanding those pathways that link Australia directly or indirectly to countries in which a pest or disease occurs is necessary to identify arriving ships with the highest likelihood of carrying hitchhiker species. This project proposes to address three important questions: 1. What general shipping pathways pose the greatest risk? 2. How to make decisions regarding what ships to search? 3. How much inspection to conduct?

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Rethinking biosecurity inspections using statistical
modelling and simulation
Petra Kuhnert and Daniel Heersink, CSIRO Data61
Dean Paini, CSIRO Health and Biosecurity
Paul Mwebaze, CSIRO Land and Water
Plant Biosecurity Cooperative Research Centre
A case study of the Asian Gypsy Moth in Australia

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Overview of the presentation
• The Asian Gypsy Moth (Lymantria dispar (L.))
• Lifecycle and biology
• Importance to the Australian economy and environment
• Current Inspection methodology
• Statistical methodology for the inspection of AGM
• Simulation of the AGM lifecycle
• Classification tree approach for classifying potential AGM hatches
• Validation and implementation
• AGM Tool
• Discussion

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Life Cycle of the Asian Gypsy Moth

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Lifecycle is complicated
Gray et al 2001 – J Insect Phys

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Current Inspection Methodology
• DAWR require a Quarantine pre-
arrival report (QPAR)
• Vessels arriving into an Australian
port
• Russian far east port/s between
40N, 60N and west of 147E
during 1 July and 30 September
• Maritime Arrivals Reporting
System or MARS
• Subsequent AGM questions
During the last 12 months, did your vessel visit a
seaport located North of 31N in China, Japan, North
Korea, Russian Far East or South Korea between 1
June and 30 September

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Methodology
• Acquire ship logs from Lloyds database for 2010
• Extract world SST for the corresponding year - NOAA
• AGM hatches simulated for each of the 3 phases and an outcome recorded
• 1 = no hatch in Australia
• 2 = potential hatch occurred but enroute to Australia
• 3 = potential hatch occurred in Australia
• Explanatory Variables:
• Temperature variables were constructed that represented the number of days during
the trip that experienced a certain temperature
<0 0-5 5-10 10-15 15-20 20-25 25-30 30-35 >35
• AGM Questions
• agmQ2: Vessel remained north of 31N for 60 consecutive days between 1 November and 30
April
• agmQ3: Vessel travelling directly to an Australian port after crossing 31N or 50S

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Generation of potential hatches
No Hatch

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Classification Tree Methodology with Bootstrapping
Three primary steps to the methodology (Breiman et al. 1984):
1. Split Criterion
• Partition data using a split criterion. for regression this is the deviance. Aim is to
partition the data into groups that minimise the RSS.
• Construct a large tree and essentially overfit.
2. Pruning
• Use cross-validation (CV) or a test set to prune the tree.
• Typically 10-fold CV is used
3. Tree Selection
• Optimal tree chosen using CV, typically the minimum.
• 1 SE rule can be implemented.
• Bootstrap approach for quantifying the reliability of node classifications (Kuhnert and
Mengersen 2003)

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Classification Tree Results

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Variable Importance Has your vessel remained either
• North of 31N, or
• South of 50S
for at least 60 consecutive days starting on
or after 1 November, but not later than 31
April?
AGM Q2:
Is your vessel travelling
directly to an Australian
port after crossing 31N
or 50S?
AGM Q3:

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Simulation of potential hatches
Potential Hatch

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AGM Tool

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Demonstration of the AGM Tool

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Discussion
• Model uses data generated through a simulation of the biological model (Gray et al. 1991, 2001;
Gray 2009)
• Very little data on inspections and hatches
• Model requires extensive validation
• Acquiring Lloyds data for an additional year
• Comparison with previous inspections and hits
• Assessment of 2017 AGM season
• Expert workshop to determine scenarios of “when to inspect”.
• Benefits of the AGM Tool
• Reduced number of vessels requiring inspections for AGM risk
• Reduced costs for the Department of Agriculture ($$)
• Minimise disruption and delay costs for shipping industry
• Implementation in MARS
• Recent papers by Gray (2016 – Decision support tool; 2017 – Impacts of climate change)

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Acknowledgments
• John Nielsen, Dave Ryan and Dominic Musolino (DAWR)
• Sanjay Boothalingam (DAWR – MARS)

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Phase I: Pre-diapause
• Modelling the rate of pre-diapause
development
• Based solely on temperature
• Increase in temperature up to a maximum
of 27.4C
• Drops sharply to zero beyond 27.4C
• Beyond 34 C developmental rate ceases.
• Key references: Gray et al. (1991); Logan et
al. (1976)
Increase in the developmental rate
maximum
( )d T

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Phase II: Diapause
• Two processes
• Models the potential developmental
response
• Models the rate of depletion of an
“inhibitor” governed by temperature
• Temperature too high: eggs will perish
• Temperature too low: eggs will not reach
post-diapause phase
• Actual developmental response (ADR) of
the egg: function of PDR, I(T) and A(T).
• Minimum and maximum temperatures
were based on experiements (Gray et al.
2001).
Process 2: Rate of depletion of inhibitor -
Inhibitor depletion Activity level depletion of inhibitor
Process 1: Potential developmental response
Potential developmental response Activity level
( )
d
I T
dt
Inhibitor

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Phase III: Post Diapause
• Post-diapause model of Gray (2009)
• Age dependent model, , is
exponential.
• Increases steadily from 25C
• Assumption that beyond 34C, eggs
do not survive
( )TR t
Developmental rate at diapause initiation