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Bovine viral diarrhoea (BVD) is an endemic disease of cattle that causes substantial losses to both beef and dairy production worldwide. The goal of this study was to estimate the prevalence of active BVD virus (BVDV) infection in beef suckler herds in Scotland. Information was collected from 301 herds using a stratified random sampling design based on agricultural census data. Herds were classified as with and without active infection based on the within-herd BVDV seroprevalence in young stock using Bayesian finite mixture modelling. This method accounted for within- and between-herd variability and allowed for classification error by the diagnostic tests. The observed sample data enabled the discrimination of three distinct seroprevalence cohorts. The results provided evidence of active BVDV infection in 16% of herds and no evidence of recent exposure in approximately two thirds of herds. The epidemiological significance of the further 16% of herds containing young stock with a median BVDV seroprevalence of 26.3-38.5% remains unclear. The fact that a large percentage of herds did not show evidence of recent infection is encouraging from an animal health and welfare perspective and the study provides a model for the further exploration of strategies aimed at BVD control at national level. [ABSTRACT FROM AUTHOR]

Analysing animal health data can be a complex task as the health status of individuals or groups of animals, might depend on many inter-related variables. The objective is to differentiate variables that are directly associated with health status and therefore promising targets for intervention, from variables that are indirectly associated with health status and can therefore at best only affect this indirectly through association with other variables. Bayesian network (BN) modelling is a machine learning technique for empirically identifying associations in complex and high dimensional data, so-called "structure discovery". An introduction to structure discovery using BN modelling is presented, comprising the key assumptions required by the methodology, along with a discussion of advantages and limitations. To demonstrate the various steps required to apply BN structure discovery to animal health data, illustrative analyses of data collected during a previously published study concerned with exposure to bovine viral diarrhoea virus in beef cow-calf herds in Scotland are presented., (Copyright © 2011 Elsevier B.V. All rights reserved.)

Using a representative sample of Scottish sheep comprising 125 flocks, the sensitivity and specificity of PCR for Jaagsiekte sheep retrovirus (JSRV) was estimated. By combining and adapting existing methods, the characteristics of the diagnostic test were estimated (in the absence of a gold standard reference) using repeated laboratory replicates. As the results of replicates within the same animal cannot be considered to be independent, the performance of the PCR was calculated at individual replicate level. The median diagnostic specificity of the PCR when applied to individual animals drawn from the Scottish flock was estimated to be 0.997 (95% confidence interval [CI] 0.996-0.999), whereas the median sensitivity was 0.107 (95% CI 0.077-0.152). Considering the diagnostic test as three replicates where a positive result on any one or more replicates results in a positive test, the median sensitivity increased to 0.279. Reasons for the low observed sensitivity were explored by comparing the performance of the test as a function of the concentration of target DNA using spiked positive controls with known concentrations of target DNA. The median sensitivity of the test when used with positive samples with a mean concentration of 1.0 target DNA sequence per 25μL was estimated to be 0.160, which suggests that the PCR had a high true (analytical) sensitivity and that the low observed (diagnostic) sensitivity in individual samples was due to low concentrations of target DNA in the blood of clinically healthy animals., (2009 Elsevier Ltd. All rights reserved.)