Quantifying evidence of a plant pest’s absence
Project ID: 1606D
CEBRA Project Leader: James Camac
DAWR Sponsor: Marion Healy
DAWR Project Leader: Bart Rossel, Ajay Niranjane, Bruce Hancocks
DAWR Division: Biosecurity Plant
MPI Project Manager:
Plant health surveillance data collected from a variety of sources is used to substantiate a decision on a pest’s status (e.g. presence, absence or incursion vs. intercept), which is captured in the Australian Plant Pest Status Database. Information records that report the absence of a pest are usually referred to as ‘negative’ surveillance data. Surveillance information (specific surveillance records, including absence information and surveillance information from 3rd parties) could be used to determine a quantifiable level of confidence for the absence of a pest and be used to determine an acceptable “threshold”. A methodology to determine the level of confidence of a pest’s status would inform the position underpinning market access requests and biosecurity decisions. Such an approach would also inform the need for enhanced surveillance information, and the nature and scope of additional information, for e.g. if the acceptable “threshold’ is not met.
Surveillance information is obtained from a variety of sources, including third party sources, such as general surveillance undertaken by farmers, scientists, tradespeople and representatives from conservation, Landcare and wildlife groups. However, the level of confidence in the outcome of information for each crop/pest surveyed is not always known and therefore may not be able to be used to support claims of area freedom or market access requests.
This project will explore alternative approaches and develop a methodology to quantify those negative surveillance data that are statistically valid for use as supportive information for specific applications. It will emphasise requirements that can be used routinely as the first step in statistically validating the establishment of pest free areas (ISPM 4) and the design of appropriate surveillance planning. The project will identify a uniform sampling strategy for collecting negative or absence data at different levels of confidence. It will also explore, as an outlook on future work, how the outputs of this statistical sampling and modelling may be combined with other relevant information such as biology, climate suitability etc. to design a framework for an effective and cost-efficient surveillance system.