Ensuring proof of pest area freedom is crucial for facilitating trade in commodities between trading partners. In situations where an import partner wishes to avoid specific pests from the exporting country, we will increasingly need robust and statistically supported evidence for proof-of-freedom. Achieving this will require data integration, advanced computational techniques, and interdisciplinary approaches.
Although large datasets are collected by government, industry and community groups for targeted and general surveillance activities, there are challenges in how to manage, integrate and analyse these data to provide the evidence needed for statements about biosecurity market access and to inform surveillance strategies and activities.
This project is supported by Forest and Wood Products Australia, with direct investment by their grower members.
Aim: This project aims to create a comprehensive catalogue of forest pest surveillance and diagnostic data collected through targeted and general biosecurity efforts at regional and national levels.
The primary objectives are to assess the suitability of these data for biosecurity purposes and explore how it can be integrated and analysed to achieve the following outcomes:
- provide statistical measures of pest area freedom, and
- identify where surveillance or diagnostic data could guide areas for improvement in biosecurity systems/programs
Meet the PhD student on this project
Yufan Zheng
“My name is Zheng Yufan, originally from China. I am currently pursuing a PhD at the ARC Training Centre in Plant Biosecurity. This project aims to integrate and analyse forest pest surveillance and diagnostic data from across Australia, in order to develop statistical methods that support pest area freedom claims for international trade. By combining interdisciplinary collaboration with data-driven approaches, the project seeks to enhance the efficiency and cost-effectiveness of Australia’s national biosecurity systems.
My research interests lie in developing practical, data-driven tools to support informed decision-making, particularly in areas such as biosecurity and epidemiology. I have extensive experience in interdisciplinary research, with demonstrated expertise in machine learning, data science, and epidemiology.”
Supervisors and advisors
Professor Richard Duncan, University of Canberra.
Professor Bernd Gruber, University of Canberra.
Dr Elle Saber, The Australian National University.
Dr Angus Carnegie, NSW Department of Primary Industries and Regional Development.
Dr Helen Nahrung, University of the Sunshine Coast.
Approach
The project involves four components:
- Create a comprehensive catalogue of forest pest surveillance and diagnostic data from relevant partner organisations and data providers across Australia, and evaluate this in terms of taxonomic, spatial, and temporal coverage.
- Develop an analytical framework that can be applied to existing data to provide evidence for pest proof-of-freedom given specific criteria.
- Identify case-study taxa, apply the analytical framework to existing data to assess evidence for proof-of-freedom for those taxa, and evaluate whether existing surveillance methods are sufficient to meet required criteria.
- Use the analytical framework to optimise/refine forest pest surveillance methods to provide evidence for proof-of-freedom at the least cost.
Want to know more? Contact yufan.zheng@canberra.edu.au.