The document discusses disease surveillance and response strategies for feral pigs in New South Wales. It outlines that the surveillance program uses risk-based and adaptive sampling to detect exotic and emerging diseases in wild pig populations. The response strategies aim to develop management plans based on reliable models of disease transmission, but current recommendations have limitations due to small data sets and assumptions. Improving models requires better data on pig movement patterns and behavior. The overall goals are to reduce risks of disease incursion and uncertainty in response, as well as mitigate impacts if incursions occur.

1. Feral Pig Disease Surveillance in NSW
Andrew Bengsen
Fabiola Silva Lee Parker Vertebrate Pest Research Unit

2. Surveillance
Active sampling strategy
for detection of exotic
and emerging diseases
in the wild
Response
Develop management
strategies based on
reliable models of
disease transmission in
the wild
Intent: Effectively detect and respond to EAD’s in wild hosts

3. People Pigs

4. Surveillance
Key components:
• Risk-based
• Adaptive sampling
• Emerging & sporadic disease

5. Surveillance
Key components:
• Risk-based
• Adaptive sampling
• Emerging & sporadic disease
• Tie in with Biosecurity ops

6. Surveillance
Key components:
• Risk-based
• Adaptive sampling
• Emerging & sporadic disease
• Tie in with Biosecurity ops
• Alternative sampling tech

7. Response
Stakes are high
Facts are uncertain
Decisions are urgent!
Uncertainty
Decision
stakes

8. Response
Key components:
• High stakes, high uncertainty
• Current recs for wild populations based on
• Small scope
• Sparse data
• Restrictive assumptions

9. Response
Key components:
• High stakes, high uncertainty
• Current recs for wild populations based on
• Small scope
• Sparse data
• Restrictive assumptions

10. Response
Key components:
• High stakes, high uncertainty
• Current recs for wild populations based on
• Small scope
• Sparse data
• Restrictive assumptions
• Sound representation of wild pop’ns in
predictive models

11. Response
• Better models need better data
• Host movement and
connectivity
• Behavioural response to
management

12. Response
• Better models need better data
• Effort:Outcomes
20%
40%
Prediction
interval
60%
80%
95%

13. Conclusion
We aim to reduce:
• risk of long sleeper incursions
• uncertainty of response
Intensity, severity and impacts of EAD
incursions
Uncertainty
Decision
stakes
Eager to collaborate
andrew.bengsen@dpi.nsw.gov.au