The aim of this study was to evaluate the utility and cost-effectiveness of a range of national surveillance methods for paratuberculosis in Irish dairy herds. We simulated alternative surveillance strategies applied to dairy cattle herds for the detection of Mycobacterium avium ssp. paratuberculosis (MAP)-infected herds (case-detection) or for estimation of confidence of herd freedom from infection (assurance testing). Strategies simulated included whole-herd milk or serum serology, serology on cull cows at slaughter, bulk milk tank serology, environmental testing, and pooled fecal testing. None of the strategies evaluated were ideal for widespread national case-detection surveillance. Herd testing with milk or serum ELISA or pooled fecal testing were the most effective methods currently available for detection of MAP-infected herds, with median herd sensitivity >60% and 100% herd specificity, although they are relatively expensive for widespread use. Environmental sampling shows promise as an alternative, with median herd sensitivity of 69%, but is also expensive unless samples can be pooled and requires further validation under Irish conditions. Bulk tank milk testing is the lowest cost option and may be useful for detecting high-prevalence herds but had median herd sensitivity <10% and positive predictive value of 85%. Cull cow sampling strategies were also lower cost but had median herd sensitivity <40% and herd positive predictive values of <50%, resulting in an increased number of test-positive herds, each of which requires follow-up herd testing to clarify status. Possible false-positive herd testing results associated with prior tuberculosis testing also presented logistical issues for both cull cow and bulk milk testing. Whole-herd milk or serum ELISA testing are currently the preferred testing strategies to estimate confidence of herd freedom from MAP in dairy herds due to the good technical performance and moderate cost of these strategies for individual herd testing. Cull cow serology and bulk tank milk sampling provide only minimal assurance value, with confidence of herd freedom increasing only minimally above the prior estimate. Different testing strategies should be considered when deciding on cost-effective approaches for case-detection compared with those used for building confidence of herd freedom (assurance testing) as part of a national program.

White spot disease (WSD) is of major economic significance to the Australian prawn industry. Since 2016, Australia has experienced several devastating outbreaks in farmed prawns. White spot syndrome virus (WSSV) has also been detected in healthy wild prawns in southeast Queensland and northern New South Wales. Subsequently, Australian prawn producers have undertaken extensive review of their on-farm biosecurity practices to mitigate against future outbreaks. In this study, we developed a stochastic scenario tree model of WSSV surveillance in the Australian prawn aquaculture industry to 1) estimate the confidence of freedom from infection in this compartment, and 2) determine the sensitivity of early detection surveillance. We also sought to understand whether the use of active surveillance during grow-out would enhance surveillance sensitivity. Australia's current approach to WSSV surveillance in the prawn aquaculture industry was found to be both sensitive and efficient to demonstrate freedom from infection over a production cycle. Early detection sensitivity, or the probability of detecting a WSSV incursion in the first affected epidemiological unit prior to onwards transmission, was low, due to the rapid spread of WSSV. Passive surveillance was, on average, more sensitive for early disease detection compared to active surveillance, even under a hypothetical scenario of daily qPCR-based testing of all grow-out ponds. The most efficient way to increase WSSV early detection sensitivity is to increase passive surveillance sensitivity by promoting awareness of the importance of rapid detection, and encouraging reporting, investigation, sampling and testing of diseased units.