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The Irish dairy industry has established a reputation for the production of safe and healthy dairy products and is seeking to further expand its export market for high value dairy products. To support its reputation, stakeholders aim to control Johne's disease. To assist decision-makers determine the most appropriate design for an Irish programme, a narrative review of the scientific literature on the epidemiology of Johne's disease, and selected control programmes throughout the world was undertaken. Two modelling studies specifically commissioned by Animal Health Ireland to assess testing methods used to demonstrate confidence of freedom in herds and to evaluate a range of possible surveillance strategies provided additional information. The majority of control programmes tend to be voluntary, because of the unique epidemiology of Johne's disease and limited support for traditional regulatory approaches. While acknowledging that test performance and sub-clinical sero-negative shedders contributes to the spread of infection, a range of socio-political issues also exist that influence programme activities. The paper provides a rationale for the inclusion of a Veterinary Risk Assessment and Management Plan (VRAMP), including voluntary whole herd testing to identify infected herds and to support assurance-based trading through repeated rounds of negative testing, national surveillance for herd-level case-detection, and improved understanding of biosecurity management practices. Identification and promotion of drivers for industry and producer engagement in Ireland is likely to guide the future evolution of the Irish Johne's Control Programme (IJCP) and further enhance its success. The provision of training, education and extension activities may encourage farmers to adopt relevant farm management practices and help them recognize that they are ultimately responsible for their herd's health and biosecurity.

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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.

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In light of the various adverse effects of Johne's disease on animal productivity and the debate on the role of its causative organism, Mycobacterium avium ssp. paratuberculosis, in the etiology of Crohn's disease, major dairy-producing countries around the world have implemented national control programs aimed at reducing the prevalence of this infection in cattle. A pilot control program was initiated in Ireland in 2013, with a key objective to provide farmers with test-negative dairy herds with tools and knowledge to increase their confidence of freedom over time. The aim of this study was to estimate the confidence of freedom obtained in test-negative Irish dairy herds over time with various sampling scenarios and to evaluate the cost-effectiveness of alternative scenarios for achieving an acceptable level of confidence of freedom in herds with no evidence of infection. A stochastic model was developed to simulate repeated annual testing of individual animals using ELISA and confirmatory assays over a period of 20 yr. Two scenarios modeled the current herd-screening options, whereas 14 alternative scenarios explored the effect of varying parameters from the current testing strategies, such as the frequency of testing, the eligibility criteria for selecting animals, the type of assay, the probability of introduction, and the assay sensitivity. Results showed that the current testing strategy with milk twice a year or serum once a year in all animals over 2 yr old provided the highest annual herd sensitivity, with a median value of 55%. Although the median confidence of freedom increased over time for all scenarios, the time required to reach 90 and 95% confidence of freedom was highly variable between scenarios. Under the testing scenario where serum tests were used once a year, the confidence of freedom reached 90% after 4 yr and 95% after 7 yr of testing. Some of the alternative scenarios achieved an acceptable level of confidence of freedom in a reasonable timeframe and at lesser cost than the current testing strategies. The results of this work are used to provide recommendations for the next phases of the program.

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The time delay to detection of an outbreak of an emergency animal disease directly affects the size of the outbreak at detection and the likelihood that the disease can be eradicated. This time delay is a direct function of the efficacy of the surveillance system in the country involved. Australia has recently completed a comprehensive review of its general surveillance system examining regional variation in both the behaviour of modelled outbreaks of foot and mouth disease and the likelihood that each outbreak will be detected and reported to government veterinary services. The size of the outbreak and the time delay from introduction to the point where 95% confidence of detection was reached showed significant (p < 0.05) regional variation with the more remote northern areas experiencing smaller outbreaks that are less likely to spread and less likely to be reported to government services than outbreaks in the more developed southern areas of Australia. Outbreaks in the more densely populated areas may take up to 43 days until a 95% confidence of detection is achieved and at that time, the outbreak may involve up to 53 farms.

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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.

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OBJECTIVE: To assess management practices and movement patterns that could influence the establishment and spread of exotic animal diseases (EAD) in pigs in Australia. METHODS: A literature review of published information and a telephone survey of 370 pig producers owning >10 pigs who were registered with the PigPass national vendor declaration scheme. RESULTS: The movement and marketing patterns of Australian pig producers interviewed were divided into two groups based predominantly on the size of the herd. Major pig producers maintain closed herds, use artificial insemination and market direct to abattoirs. Smaller producers continue to purchase from saleyards and market to other farms, abattoirs and through saleyards in an apparently opportunistic fashion. The role of saleyards in the Australian pig industry continues to decline, with 92% of all pigs marketed directly from farm to abattoir. CONCLUSIONS: This survey described movement patterns that will assist in modelling the potential spread of EAD in the Australian pig industry. Continued movement towards vertical integration and closed herds in the Australian pig industry effectively divides the industry into a number of compartments that mitigate against the widespread dissemination of disease to farms adopting these practices.

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Australia is a major exporter of livestock and livestock products; a trade assisted by a favourable animal health status. However, increasing international travel and trade, land use changes and climatic change increase the risks of exotic and emerging diseases. At the same time, public sector resources for managing these risks are static or declining. Animal health authorities in Australia identified the need to develop a consistent national approach to surveillance that allocates resources according to risk. A study was undertaken to assess the relative likelihood of occurrence of eight significant diseases of concern to animal health authorities with the aim of producing risk maps to better manage animal disease surveillance. The likelihood of disease occurrence was considered in terms of the likelihood that a disease is introduced and the likelihood that the disease establishes and spreads. Pathways for introduction and exposure and for establishment and spread were identified and data layers representing the factors contributing to each pathway produced as raster maps. A multi-criteria analysis process was used to combine data layers into pathways and pathways into likelihood maps using weightings that reflect the relative importance of each layer and pathway. The likelihood maps for introduction and exposure and for establishment and spread were combined to generate national likelihood maps for each disease. To inform Australia's general surveillance system that exists to detect any disease of importance, the spatial profiles of the eight diseases were subsequently combined using weightings to reflect their relative consequences. The result was a map of relative likelihood of occurrence of any significant disease. Current surveillance activity was assessed by combining data layers for government disease investigations, proximity to vets and wildlife disease investigations. Comparison of the overall risk and current surveillance maps showed that the distribution of current effort was well matched to the distribution of risk.

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Herd classification is a key component of national Johne's disease (JD) control programs. Herds are categorized on the basis of test results, and separate sub-programs are followed for test-positive and test-negative herds. However, a test-negative herd result does not necessarily equate to JD freedom for reasons relating to disease pathogenesis and available diagnostic tests. Thus, in several countries, JD control programs define test-negative herds as having a "low risk" of infection below a specified prevalence. However, the approach is qualitative, and little quantitative work is available on herd-level estimates of probability of freedom in test-negative herds. This paper examines the effect over time of alternative testing strategies and bio-exclusion practices on JD risk in test-negative herds. A simulation model was developed in the programming language R. Key model inputs included sensitivity and specificity estimates for 3 individual animal diagnostic tests (serum ELISA, milk ELISA, and fecal culture), design prevalence, testing options, and testing costs. Key model outputs included the probability that infection will be detected if present at the design prevalence or greater (herd sensitivity; SeH), the probability that infection in the herd is either absent or at very low prevalence (i.e., less than the design prevalence; ProbF), the probability of an uninfected herd producing a false-positive result [P(False+)], and mean testing cost (HerdCost) for different testing strategies. The output ProbF can be updated periodically, incorporating data from additional herd testing and information on cattle purchases, and could form the basis for an output-based approach to herd classification. A high ProbF is very difficult to achieve, reflecting the low sensitivity of the evaluated tests. Moreover, ProbF is greatly affected by any risk of introduction of infection, decreasing in herds with poor bio-exclusion practices despite ongoing negative test results. The value of P(False+) was substantial when tests with imperfect specificity were used. Testing strategies can substantially influence testing costs but with little effect on test performance. This study illustrates an output-based approach to herd classification, with potential for national and field applications.

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This observational study was undertaken in order to evaluate the diagnostic specificity of the blocking enzyme-linked immunosorbent assay (bELISA) for serum antibodies to influenza A virus nucleoprotein during the equine influenza (EI) outbreak response in New South Wales, Australia, in 2007. Using data collected during the outbreak response, bELISA testing data were collated for assumed uninfected horses from areas where EI infection was never recorded. Diagnostic specificity of the bELISA used during the EI response was high, but varied significantly between some regions, although the reason(s) for this variation could not be determined.

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Equine influenza (EI) was first diagnosed in the Australian horse population on 24 August 2007 at Centennial Park Equestrian Centre (CPEC) in Sydney, New South Wales (NSW), Australia. By then, the virus had already spread to many properties in NSW and southern Queensland. The outbreak in NSW affected approximately 6000 premises populated by approximately 47,000 horses. Analyses undertaken by the epidemiology section, a distinct unit within the planning section of the State Disease Control Headquarters, included the attack risk on affected properties, the level of under-reporting of affected properties and a risk assessment of the movement of horses out of the Special Restricted Area. We describe the epidemiological features and the lessons learned from the outbreak in NSW.

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The aim of this preliminary study was to estimate the proportions of seropositive horses on infected premises (IPs) in order to assess the attack risk of the disease. Logistic regression analyses were conducted to evaluate the differences in attack risks between enterprise sizes and predefined spatial clusters/regions. The average attack risk experienced during the outbreak was 96.88% (median 100%), but it differed according to the size of the enterprise and other geographic and demographic conditions. The highest attack risks were observed in the Dubbo cluster/region and the lowest in the Narrabri-Northern cluster. Properties with fewer horses were generally more likely to have higher attack risks than larger enterprises, though this was not true for all cluster/regions.

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During the 2007 equine influenza (EI) outbreak in Australia, there was no objective information about the possible under-reporting of cases by horse owners either so that they would avoid movement restrictions or because of their inability to detect infection. This investigation aimed to estimate the proportion of under-reporting during the outbreak based on the results of surveillance undertaken in conjunction with vaccination. The results provided improved estimates of morbidity during the outbreak and indicated the level of under-reporting likely to occur in future outbreaks of other infectious diseases in horses in Australia.

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Australia has the world's largest population of wild equids and equine influenza (EI) was confirmed on several properties in New South Wales (NSW) close to uncontrolled areas of land during the 2007 outbreak. Likelihood and risk management assessments were carried out to determine the risk of EI infection of the wild horse populations. The likelihood of spread to the wild horse population was determined to be extremely low, but the likelihood of spread from an established wild horse reservoir back to domestic horses was considered high. The most effective mechanism of control was determined to be prevention of the spread of EI into the wild horse population through a vaccination buffer zone between EI infection foci and known wild horse populations.

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Simulation models were developed to quantify the likelihood of equine influenza virus infection entering pre-movement isolation, persisting through pre- and post-movement isolation periods without being detected by scheduled laboratory testing, and escaping to infect susceptible horses at a destination. The mean probability of escape ranged from 1 in 1,200,000 to 1 in 600,000 depending on lot size. For 95% of iterations the probability of escape was less than 1 in 200,000, regardless of lot size. For a large group of 600 horses processed as multiple separate lots, the mean probability of escape ranged from 1 in 10,000 to 1 in 56,000 depending on lot size. As a result of this analysis, a modified protocol, with two tests during pre-movement isolation and an additional test during post-movement isolation at the Chief Veterinary Officer's discretion, was implemented.

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OBJECTIVE: To quantify the probability of freedom from equine influenza (EI) in New South Wales (NSW), Australia, based on analysis of polymerase chain reaction (PCR) testing. METHODS: Testing in the infected areas of NSW during the period 1 January to 30 April 2008. RESULTS: Data from the random survey were collated and analysed to provide estimates of the probability of detecting EI if it was present at a prevalence ranging from 0.01% to 0.5%. The sensitivity estimates were then combined with a prior estimate of the probability of freedom in a simulation model, to estimate the posterior probability of freedom from EI (given the negative results of the random survey). CONCLUSIONS: The very large volume of PCR tests performed provided a very high level of confidence that EI had been successfully eradicated from NSW.

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Paratuberculosis is a chronic infection affecting cattle and other ruminants. In the dairy industry, losses due to paratuberculosis can be substantial in infected herds and several countries have implemented national programmes based on herd-classification to manage the disease. The aim of this study was to develop a method to estimate the probability of low within-herd prevalence of paratuberculosis for Danish dairy herds. A stochastic simulation model was developed using the R programming environment. Features of this model included: use of age-specific estimates of test-sensitivity and specificity; use of a distribution of observed values (rather than a fixed, low value) for design prevalence; and estimates of the probability of low prevalence (PrLow) based on a specific number of test-positive animals, rather than for a result less than or equal to a specified cut-point number of reactors. Using this model, five herd-testing strategies were evaluated: (1) milk-ELISA on all lactating cows; (2) milk-ELISA on lactating cows4 years old; (4) faecal culture on all lactating cows; and (5) milk-ELISA plus faecal culture in series on all lactating cows. The five testing strategies were evaluated using observed milk-ELISA results from 19 Danish dairy herds as well as for simulated results from the same herds assuming that they were uninfected. Whole-herd milk-ELISA was the preferred strategy, and considered the most cost-effective strategy of the five alternatives. The five strategies were all efficient in detecting infection, i.e. estimating a low PrLow in infected herds, however, PrLow estimates for milk-ELISA on age-cohorts were too low in simulated uninfected herds and the strategies involving faecal culture were too expensive to be of practical interest. For simulated uninfected herds, whole-herd milk-ELISA resulted in median PrLow values>0.9 for most herds, depending on herd size and age-structure. None of the strategies provided enough power to establish a high PrLow in smaller herds, or herds with a younger age-structure. Despite this, it appears as if the method is a useful approach for herd-classification for most herds in the Danish dairy industry.

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OBJECTIVE: To compare estimates of ovine Johne's infection prevalence produced by several alternate methods based on pooled faecal culture (PFC) results with prevalence estimates based on individual faecal culture (IFC). PROCEDURE: Seven methods for estimating prevalence of infection based on PFC results were incorporated in a computer program, including methods for imperfect test sensitivity and specificity, for variable pool size and a Bayesian method that incorporates prior knowledge about test performance and prevalence. These methods were then used to analyse PFC data at one observation 30 months post-vaccination in a field trial of a killed vaccine for the control of OJD, undertaken on three farms in New South Wales. RESULTS: Prevalence estimates, for three methods that assume a perfect test, were close to the IFC estimate, whereas for three other methods that assume an imperfect test, the estimated prevalence was generally higher than the IFC estimate. In comparison, the Bayesian approach produced more variable estimates that were substantially higher than the IFC estimate when an inappropriately high prior estimate of prevalence was used. CONCLUSION: Despite the limitations of each method, two methods provided accurate and reasonable estimates of the prevalence assessed by IFC in all instances, and are appropriate for the analysis of data from this vaccine trial. One of these methods also has the advantage of allowing for variable pool size. However, further research is needed to develop a method that will simultaneously account for variation in pool size and in test sensitivity and specificity.

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A method for quantitative evaluation of surveillance for disease freedom has been presented in the accompanying paper (Martin et al., 2007). This paper presents an application of the methods, using as an example surveillance for classical swine fever (CSF) in Denmark in 2005. A scenario tree model is presented for the abattoir-based serology component of the Danish CSF surveillance system, in which blood samples are collected in an ad hoc abattoir sampling process, from adult pigs originating in breeding herds in Denmark. The model incorporates effects of targeting (differential risk of seropositivity) associated with age and location (county), and disease clustering within herds. A surveillance time period of one month was used in the analysis. Records for the year 2005 were analysed, representing 25,332 samples from 3528 herds; all were negative for CSF-specific antibodies. Design prevalences of 0.1-1% of herds and 5% of animals within an infected herd were used. The estimated mean surveillance system component (SSC) sensitivities (probability that the SSC would give a positive outcome given the animals processed and that the country is infected at the design prevalences) per month were 0.18, 0.63 and 0.86, for among-herd design prevalences of 0.001, 0.005 and 0.01. The probabilities that the population was free from CSF at each of these design prevalences, after a year of accumulated negative surveillance data, were 0.91, 1.00 and 1.00. Targeting adults and herds from South Jutland was estimated to give approximately 1.9, 1.6 and 1.4 times the surveillance sensitivity of a proportionally representative sampling program for these three among-herd design prevalences.

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OBJECTIVE: To estimate the sensitivity of the ELISA used in dairy cattle herds participating in the Victorian Bovine Johne's Disease Test and Control Program (TCP). PROCEDURE: The percentage of ELISA reactors in age and test cohorts was estimated from age-specific test data derived from TCP herds with long testing histories. Age-distribution data from production-tested herds enabled estimation of reactor rates in animals that were culled or died. RESULTS: ELISA sensitivities at the first test round in herds achieving five, six and seven annual herd tests were 16.1, 14.9 and 13.5% respectively. The ELISA sensitivity in 2, 3 and 4-year-old animals at the first test round in herds testing seven times was 1.2, 8.9 and 11.6% respectively but remained between 20 and 30% in older age-groups. CONCLUSION: The sensitivity of the ELISA is considerably lower than previous estimates, probably because previous estimates were predominantly measured against faecal culture, which has subsequently been shown to have low sensitivity itself, and did not appreciate the long period that appears to precede detectable faecal excretion in most animals.

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The sensitivities and specificities of an absorbed enzyme-linked immunosorbent assay (ELISA) and an agar-gel immuno-diffusion (AGID) test for the detection of Johne's disease in sheep were estimated using data from six known infected and 12 assumed uninfected sheep flocks. Sensitivities were estimated for all histologically positive sheep, as well as by histological lesion score, lesion type (paucibacillary or multibacillary) and sheep body-condition score, with ELISA sensitivities estimated at 95 and 99% specificity. Logistic-regression analysis was used to test for significant effects of lesion score and condition score, with flock included in the model as a random effect. Estimated specificities were 95% (95% CI: 93.4, 95.6%) and 99% (98.4, 99.4%) for ELISA cut-point ratios of 2.4 and 3.6, respectively, and 100% (99.7, 100.0%) for the AGID. Estimated sensitivities for all infected sheep were 41.5% (35.0, 48.3%), 21.9% (16.6, 27.9%) and 24.6% (19.1, 30.7%) for ELISA cut-point ratios of 2.4 and 3.6 and for AGID, respectively. Sensitivities of all tests and cut-points varied significantly between flocks and between categories of lesion score and condition score. Sensitivity ranged from 25 to 73, 10 to 47 and 9.2 to 63% between flocks, for the ELISA with cut-points of 2.4 and 3.6, and for the AGID, respectively. Sensitivity was highest in thin sheep and in sheep with multibacillary lesions. The effects of lesion type and condition score on test sensitivity were significant in the logistic regressions for the AGID and ELISA at both cut-points and the flock effect was significant for the AGID but not for the ELISA at either cut-point.

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