4 Post-border surveillance and area freedom
Biosecurity surveillance fulfils different purposes. It is critical for (i) early detection of incursions and outbreaks of pests and diseases, (ii) supporting control and eradication efforts following an incursion or outbreak, and (iii) long-term management of established pests and diseases. Surveillance also provides evidence for proof of freedom and area freedom claims. The Department uses pest freedom information in market access negotiations that support Australian exporting industries.
Emergency preparedness and planning of surveillance are an important part of post-border activities because they support response activities and improve the likelihood of detection of incursions or outbreaks of pests and diseases. In terms of emergency preparedness, for example, up-to-date farm livestock demographics information supports decision making during an emergency response or investigation into an outbreak. CEBRA tested different approaches for modelling counts of livestock units or cattle using data from New Zealand. One of the main findings of this study highlighted the need for a single database for national level animal demographic data. The study also identified the strengths and weaknesses of the available national-level data sets that underlie biosecurity response and disease preparedness, enabling decision-makers to make more informed future decisions about farm livestock demographic information.30
Quote 7: Dr. Mary Van Andel (Ministry for Primary Industries)
CEBRA’s work, in close collaboration with MPI staff, provided high-quality actionable insights about the gaps and interoperability of New Zealand’s livestock databases, greatly enhancing MPI’s ability to respond to a cattle disease incursion and identifying opportunities to improve the system going forwards.
To support the planning and design of surveillance programs, CEBRA reviewed tools and methods for managers to plan, implement and evaluate post-border surveillance activities and also developed guidelines and training materials for these tools. CEBRA also expanded the use of EpiTools, an online set of tools for selection of survey design, to the plant surveillance context. As a consequence of CEBRA’s work, EpiTools were used in a Banana Freckle outbreak in the Northern Territory.31,32,33,34,35,36,37 A part of surveillance planning can be to estimate survey effort for detecting invasive plant species. CEBRA developed a novel method for determining the average survey time needed to detect weeds within a native vegetation community.38 In a current project CEBRA aims to develop criteria for prioritisation of plant pests for surveillance in Australia.
CEBRA worked on a number of projects that addressed the different purposes of surveillance. Research into early detection included a review of the use of new technologies for rapid, field-based testing to detect major emergency animal diseases, such as avian influenza, foot-and-mouth disease (FMD), anthrax, classical swine fever and mad cow disease. The project outputs are relevant to the office of the Australian Chief Veterinary Officer, who chairs the consultative committee on emergency animal diseases in the event of an emergency animal disease outbreak.35,36,37 In the case of FMD, early detection can significantly reduce the impacts of outbreaks. CEBRA found that bulk milk testing on top of passive surveillance is currently not economically justified prior to an incursion, due to high costs of testing and a low frequency of outbreaks. However, bulk milk testing is well-suited for post-outbreak active surveillance to shorten the length of time and size of an epidemic.39 CEBRA is currently applying a simulation approach combined with an economic optimisation framework to determine the right mix of appropriate active and enhanced passive surveillance for detection of FMD.
Following an incursion or outbreak, surveillance supports the emergency response and tracing activities. CEBRA developed a simulation model that helps to prioritise surveillance of movements of pests and pathogens from infected properties. This model is flexible to be used in different incursion or outbreak scenarios but needs to be adapted for each situation. It was developed as a standalone library within R, which is a free, open-source statistical software.40,41,42 For the ongoing measure of the progress of a weed eradication program CEBRA developed an Excel based monitoring tool (MoniTool) that produces a graphical output of progress over time.33,34
Scientific evidence of pest-free status is important for assurance to trading partners. CEBRA confirmed the suitability of a new method for determining pest free status in relation to plant pests and diseases and invasive plants species. It was originally developed for assessment of animal disease status.43 In an alternative technical approach for establishing area freedom for animal disease management, and building on this new method, CEBRA presented analogous analysis methods that can be simpler to implement while providing the same results.44
ISPM 31 prescribes an approach to sampling for proof of freedom that is based on specifying a probability that the pest will be detected if it is present at a nominated prevalence. However, the approach provides no insight as to how the probability or prevalence should be set. CEBRA developed an approach that helps agencies to minimise the net expected costs of surveillance and the expected damages that would occur, should the pest still be present.45 By wrapping the existing approach in an economic framework, we shed light on the implications of different choices from the point of view of making decisions. The report illustrates the method’s implementation using information contained in two historical proof of freedom proposals conducted in Queensland for cocoa pod borer (Conopomorpha cramerella) and citrus canker (Xanthomonas axonopodis). The tool is provided online at https://apps.cebra.unimelb.edu.au/pof/.
References
30. Andel, M. van et al. (2016). National-level farm demographic data for preparedness of highly-infectious livestock disease epidemics. Centre of Excellence for Biosecurity Risk Analysis, report 1402C.
31. Fox, D. (2007). Statistical methods for biosecurity monitoring. Australian Centre of Excellence for Risk Analysis, report 0605 ID1.
32. Fox, D. (2009). Statistical methods for biosecurity monitoring and surveillance. Australian Centre of Excellence for Risk Analysis, report 0605 ID2.
33. Hester, S. & Herbert, K. (2012). MoniTool - an eradication monitoring tool - Manual. Australian Centre of Excellence for Risk Analysis, report 1004A ID6 1.
34. Hester, S., Sergeant, E., Herbert, K. & Robinson, A. (2012). Post-border surveillance techniques: Review, synthesis and deployment. Australian Centre of Excellence for Risk Analysis, report 1004A ID5.
35. Sims, L. (2012). The use of new technologies for rapid, field-based (point-of-care) testing in the detection of emergency animal diseases. Australian Centre of Excellence for Risk Analysis, report 1004B.
36. Cox-Witton, K., Reiss, A. & Woods, R. (2011). Zoo Based Wildlife Disease Surveillance Pilot Project. Australian Centre of Excellence for Risk Analysis, report 1004B ID5.
37. Hester, S. & Sergeant, E. (2012). Post-border surveillance techniques: Review, synthesis and deployment – Sub-Project 2E ’Proof-of-freedom’ toolbox. Australian Centre of Excellence for Risk Analysis, report 1004B ID9.
38. Garrard, G., Bekessy, S. & Wintle, B. (2009). Determining necessary survey effort to detect invasive weeds in native vegetation communities. Australian Centre of Excellence for Risk Analysis, report 0906.
39. Kompas, T. et al. (2015). Optimal Surveillance against Foot-and-Mouth Disease: The Case ofBulk Milk Testing in Australia. Centre of Excellence for Biosecurity Risk Analysis, report 1304A.
40. Potts, J., Christian, R. & Burgman, M. (2012). Model-based search strategies for plant diseases: A case study using citrus canker (Xanthomonas citri). Australian Centre of Excellence for Risk Analysis, report 1006B ID2.
41. Potts, J. M. et al. (2013) Model-based search strategies for plant diseases: A case study using citrus canker (Xanthomonas citri). Diversity and Distributions 19, 590–602
42. Potts, J. (2014). TRACE: An R-package to trace pest spread via multiple dispersal mechanisms. Australian Centre of Excellence for Risk Analysis, report 1206B.
43. Martin, T. (2009). Combining disparate data sources to demonstrate pest/disease status. Australian Centre of Excellence for Risk Analysis, report 0703.