Assessing the efficacy of general surveillance for detection of incursions of livestock diseases in Australia

- We present a model for evaluating general surveillance for livestock disease.
- We use the example of detection of foot and mouth disease in Australia.
- Each of 12 regions input locally applicable farmer-related values.
- Regional median probability of detecting disease on a single farm was 23-52%.
- Regional median time elapsed before disease was detected was 20-33 days.
- Recognition of disease by small landholders was important for overall detection.

Australia, as a relatively isolated country with a high level of agricultural production, depends on, and has the opportunity to maintain, freedom from a range of important diseases of livestock. Occasional incursions of such diseases are generally detected by 'passive', general surveillance (GS). In current surveillance planning, a risk-based approach has been taken to optimising allocation of resources to surveillance needs, and having mapped the relative risk of introduction and establishment of diseases of concern, a means of mapping the efficacy of GS for their detection was required, as was a means of assessing the likely efficacy of options for improving GS efficacy if needed. This paper presents the structure and application of a tool for estimating the efficacy of Australia's GS, using the example of foot and mouth disease (FMD). The GS assessment tool (GSAT) is a stochastic spreadsheet model of the detection, diagnosis and reporting of disease on a single infected farm. It utilises the output of an intraherd disease spread model to determine the duration and prevalence of infection on different types of farm. It was applied separately to each of twelve regions of Australia, demarcated by dominant livestock production practices. Each region supplied estimates of probabilities relevant to the detection of FMD, for each of fourteen farm types and all species susceptible to the disease. Outputs of the GSAT were the average probability that FMD on the farm would be detected (single farm sensitivity), the average time elapsed from incursion of the disease to the chief veterinary officer (CVO) being notified (time to detection), and the number of average properties that would need to be infected before the CVO could be 95% confident of detecting at least one. The median single farm sensitivity for FMD varied among regions from 0.23 to 0.52, the median time to detection from 20 to 33 days, and the number of properties infected for 95% confidence of detecting at least one from 4 to 12. The GSAT has proved a valuable tool in planning surveillance for detection of exotic livestock disease in Australia, and it provides a practical example of the use of probabilistic modelling to answer important questions in the face of imperfect information.