Session 5: Decision making for eradication and quarantine zones

biosecurity built on science
Decision making for surveillance and quarantine
Grant Hamilton & Peter Baxter
Plant Biosecurity Cooperative Research Centre

What is the problem?
• Urgent need for efficient and effective methods to plan
surveillance and quarantine
• Incorporate multiple layers of data to better plan surveillance
and qzones
• Decisions in the face of uncertainty
• initially with limited data
• how to obtain new data
• how to incorporate new data into the decision response
Briefly summarize the specific problem or issue that your research is addressing?

What are we doing about it?
How will your research address the problem or issue?
 create applied methods that support data capture and
decision making
- UAVs –effective flight paths
- Optimising methods for surveillance, qzones –risk maps and
networks
- Spatial analysis- Qfly –natural barriers


Planning effective surveillance for detection
UAV
Unmanned aerial vehicle (UAV) surveillance
 how do flight-plans perform faced with
- detection errors
- organism’s spatial ecology
Best performing UAV flight plans
underlyingdetectionerror
aggregation
Infestationintensity
Baxter & Hamilton (2015). MODSIM2015: 1393-1398
Fine-tuning of unmanned aerial surveillance for ecological systems.
high
+fast

Range of recommendations
• eg
• Small detection error, high density, regular spatial pattern = Low and
Fast flights
• Detecting an incursion – High and Fast flights (for moderate to low
detection error)

Results
Example of
incursion
response
Randomised risk-weighted search
• Sites
+ Infected
□ Searching
X Detected
If ALWAYS looking near
Infected property, sub optimal
result
Ensure surveillance is not too
narrowly focused
Next step is to translate
surveillance into..

Surveillance prioritisation through time
.
week: 100 300 400Priortzn
basis:
Risk =
proximity
to known
infections
Risk =
Estimated
natural &
social
spread
spread of infection

Adding extra layers: transmission risk and control
using networks
Risk networks and Incursion Response rules
Multiple networks
• Human:
• “socio-economic” ( informed by tracing data)
• road
• agronomists as vectors
• Abiotic:
• environmental gradient
• extreme-event mixing
15 farms; 3 agronomists cover 10 farms

Farm network
Quarantine approaches
Shows extra effect of agronomists
(note increased scale of connection strength)

Networks of Panama tracing
• Real tracing data from the Panama incursion:
• 336 tracing connections
• Data manipulation for this:
• Excluded non-banana properties
• Recategorised into 5 types of connection. Listed by decreasing risk:
• Plant material e.g. planting material, debris …
• Equipment sharing e.g. irrigation, earth-movers …
• People movement e.g. crop consultants, packers …
• Geographic links e.g. proximity, shared drainage line …
• Other e.g. rubbish collection, fuel delivery of
• Randomly assigned positions in space to preserve anonymity

Tracing through a farm network
Shared plant material only

Equipment sharing only

Geographic links

Combined links – assuming all bi-directional

Transmission risk and control
Risk networks and Incursion Response rules
Quarantine rules from human network links:
Blanket:
• moderate widespread restrictions
Targeted:
• IP’s isolated, weaker widespread restrictions
Path-based:
• reduce all road and agronomist links
Link-based:
• reduce connections from sites within fixed radius of IP’s
Surveillance
• Risk-based heuristic to optimised search method (Parnell et al. 2014)

Results
No management
(mean±SE, 1000 runs/simulation)
Infection intensity,
hectares
(“fungal
load”)
# farms infected

Results
Effect of different quarantine measures
(mean±SE)
Blanket Targeted Path-based Link-based
hectares
# farms infected
Quarantine:
reduce connections from sites within
fixed radius of IP’s

Results
Effect of different quarantine measures
(mean±SE)
Blanket Targeted Path-based Link-based
hectares
# farms infected
Quarantine:
For latest results – Peter
Baxter at 1:30pm Thursday

QFLY population genetics project:
Image: www.goulburnrivervalley.com.au/stakeholderinformation/are-you-giving-accurate-information-about-fruit-fly
 Aim: to examine the population
genetic structure of QFLY in the
former Fruit Fly Exclusion Zone
- If genetic structure exists, link barriers
to gene flow with landscape features
- Identify putative source(s) of current
infestation

Qfly Genetics: progress to date
 10 microsatellite markers screened
 279 flies from 23 sites
 high levels of polymorphism for most loci
Locus Ho Ht # Alleles Allelic richness
Bt32 0.753 0.830 16 7.034
Bt11 0.699 0.738 9 4.734
1.7.7 0.035 0.035 5 1.311
Bt4.1A 0.306 0.522 6 2.403
Bt15 0.429 0.694 5 3.953
Bt14 0.559 0.587 5 3.861
Bt10 0.469 0.558 9 4.082
Bt1.7 0.642 0.736 15 5.515
Bt8.6A 0.733 0.847 22 7.221
Bt17 0.703 0.720 6 4.305
Site Sample Size
Bilbul 6
Cobram South 22
Coleambally 2
Corbie Hill 25
Darlington Point 13
Griffith 9
Hanwood 11
Hillston 18
Kialla Central 16
Leeton 14
Merbein 27
Mooroopna 23
Murrami 5
Narrandera 1
Nericon 9
Paytners Sliding 20
Stanbridge 5
Stoney Point 5
Summerton Park 21
Tharbogang 12
Whilton 6
Yanco 2
Yenda 7
NB: Blue sites located in Victoria

Results: population genetic structure analysis
StoneyPt
Stanbridge
Yenda
Yanco
Bilbul
CobramSth
Coleambally
CorbieHill
DarlingtonPt
Griffith
Hanwood
Hillston
KiallaCentral
Leeton
Merbein
Mooroopna
Nericon
Narrandera
PaytnersSliding
SummertonPk
Whilton
Tharbogang
Murrami
 2 population genetic clusters identified in structure analysis

Population genetic
structure analysis
Paytners Sliding
Stanbridge Stoney Point
Yanco
Yenda
Tharbogang
Whilton
Murrami
Narrandera
Nericon
Hanwood
Hillston
Leeton
Coleambally
Darlington Pt
Griffith
Bilbul
Corbie Hill
Riverina Protected Areas
Each pie graph shows the proportion of individuals with that had
a proportion of membership of >0.8 to one of the 2 identified
clusters Grey = unssigned (<0.8)
 Cluster 1 (green) found across
the two sections of the Riverina
Protected Areas and in southern
sites on the Victorian border
- No barriers to gene flow
identified within or between the
Riverina Protected Areas
- gene flow occurs between FFEZ
and southern Victorian sites
- Similar result to findings of
Gilchrest & Meats 2010

Population genetic structure in the former FFEZ
Cluster 2 (red) found only at
Mildura in the pest free area
 Represents distinct population to
that in Riverina Protected Areas
 very little gene flow between the
two areas

Future: next steps
 To delineate the extent of the ‘red’ cluster and identify
gene flow and possible barriers, more samples are required
from Renmark and the Pest Free Area
 Collections have been made (from Renmark and along the
Murray River between north of Wentworth to Echuca) and
samples due to arrive soon:
Samples yet to be processed
Site # of flies
Barham, NSW 23
Boundary Bend, VIC 17
Ellerslie, NSW 13
Kenley, VIC 7
Lake Boga, VIC 21
Robinvale, VIC 11
Swan Hill, VIC 14
Tooleybuc, NSW 21
Vinifera, VIC 17
Bairnsdale, VIC 11
Cohuna, VIC 20
Echuca, VIC 26
Renmark ?

End-User Advocate’s Perspective
“This project will create models and processes to assist with surveillance, decision
making for quarantine zones and eradication strategies.
Uncertainty is symptomatic of many biosecurity responses, and its treatment and
depiction in these model outputs could prove useful as a guide to future similar
incursions.
The model framework will be adaptable to diverse future incursions … providing insights
into how decisions are made during the response, [and] … leading to outputs and tools
that can actually be used to change future incursion responses.
Other aspects of the project … provide insight into strategies of hierarchical surveillance.
- Mike Ashton, QDAF, End-User Advocate PBCRC-2100

Who will benefit from this research?
End users
 state government
- smart, dynamic decisions throughout response
 surveillance providers (governmental or commercial)
 Farmers and communities in affected areas
Beneficiaries
 state governments and Australia
- pest free declarations etc.
 horticultural growers/industry
- bananas, citrus, many more

Delivery & Future
• Software tool to aid decision making for surveillance and quarantine
zones
• Used by post incursion decision makers, surveillance planners
• Training in how to use
• Academic papers and conference presentations
• Software currently focused on TR4 – look for ways to apply to other
systems
• Extend theoretical analysis of networks

Thank you
• For more information, please email:
• g.hamilton @qut.edu.au
PBCRC is established and supported under
the Australian Government Cooperative
Research Centres Programme
Project team:
 Bernie Dominiak (NSW)
 Ceri Pearce (Qld)
 Rebecca Sappupo (Qld)
 John Weiss (Vic)
 Rune Rasmussen (QUT)
 Susan Fuller (QUT)

Session 5: Decision making for eradication and quarantine zones

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Session 5: Decision making for eradication and quarantine zones