The number of cattle herds placed under movement restrictions in Great Britain (GB) due to the suspected presence of bovine tuberculosis (bTB) has progressively increased over the past 25 years despite an intensive and costly test-and-slaughter control program. Around 38% of herds that clear movement restrictions experience a recurrent incident (breakdown) within 24 months, suggesting that infection may be persisting within herds. Reactivity to tuberculin, the basis of diagnostic testing, is dependent on the time from infection. Thus, testing efficiency varies between outbreaks, depending on weight of transmission and cannot be directly estimated. In this paper, we use Approximate Bayesian Computation (ABC) to parameterize two within-herd transmission models within a rigorous inferential framework. Previous within-herd models of bTB have relied on ad-hoc methods of parameterization and used a single model structure (SORI) where animals are assumed to become detectable by testing before they become infectious. We study such a conventional within-herd model of bTB and an alternative model, motivated by recent animal challenge studies, where there is no period of epidemiological latency before animals become infectious (SOR). Under both models we estimate that cattle-to-cattle transmission rates are non-linearly density dependent. The basic reproductive ratio for our conventional within-herd model, estimated for scenarios with no statutory controls, increases from 1.5 (0.26–4.9; 95% CI) in a herd of 30 cattle up to 4.9 (0.99–14.0) in a herd of 400. Under this model we estimate that 50% (33–67) of recurrent breakdowns in Britain can be attributed to infection missed by tuberculin testing. However this figure falls to 24% (11–42) of recurrent breakdowns under our alternative model. Under both models the estimated extrinsic force of infection increases with the burden of missed infection. Hence, improved herd-level testing is unlikely to reduce recurrence unless this extrinsic infectious pressure is simultaneously addressed.

Bovine Tuberculosis (bTB) in cattle is a global health problem and eradication of the disease requires accurate estimates of diagnostic test performance to optimize their efficiency. The objective of this study was, through statistical meta-analyses, to obtain estimates of sensitivity (Se) and specificity (Sp), for 14 different ante-mortem and post-mortem diagnostic tests for bTB in cattle. Using data from a systematic review of the scientific literature (published 1934-2009) diagnostic Se and Sp were estimated using Bayesian logistic regression models adjusting for confounding factors. Random effect terms were used to account for unexplained heterogeneity. Parameters in the models were implemented using Markov Chain Monte Carlo (MCMC), and posterior distributions for the diagnostic parameters with adjustment for covariates (confounding factors) were obtained using the inverse logit function. Estimates for Se and/or Sp of the tuberculin skin tests and the IFN-γ blood test were compared with estimates published 2010-2015. Median Se for the single intradermal comparative cervical tuberculin skin (SICCT) test (standard interpretation) was 0.50 and Bayesian credible intervals (CrI) were wide (95% CrI 0.26, 0.78). Median Sp for the SICCT test was 1.00 (95% CrI 0.99, 1.00). Estimates for the IFN-γ blood test Bovine Purified Protein Derivative (PPD)-Avian PPD and Early Secreted Antigen target 6 and Culture Filtrate Protein 10 (ESAT-6/CFP10) ESAT6/CFP10 were 0.67 (95% CrI 0.49, 0.82) and 0.78 (95% CrI 0.60, 0.90) respectively for Se, and 0.98 (95% CrI 0.96, 0.99) and 0.99 (95% CrI 0.99, 1.00) for Sp. The study provides an overview of the accuracy of a range of contemporary diagnostic tests for bTB in cattle. Better understanding of diagnostic test performance is essential for the design of effective control strategies and their evaluation.

Selected demographic features and trends in bovine tuberculosis (BTB) from 1995 to 2010 are described for the countries of the UK and the Republic of Ireland, using standardised definitions and measures. All countries experienced a reduction in the number of cattle and herds and in the proportion of dairy herds, while average herd size increased. In general, the trends indicate a stable situation of very low BTB prevalence in Scotland and, over most of the period, a rising prevalence in England and Wales. The prevalence in the Republic of Ireland declined while Northern Ireland experienced both a rise and fall. Differences in demography, BTB programme structure and test results were noted, particularly between the island of Ireland and Great Britain. Further investigation of these differences may provide valuable insights into risk factors for BTB and optimisation of existing BTB programmes.

Bovine tuberculosis (bTB) is an important disease of cattle caused by infection with Mycobacterium bovis, a pathogen that may be extremely difficult to eradicate in the presence of a true wildlife reservoir. Our objective was to identify and review relevant literature and provide a succinct summary of current knowledge of risk factors for transmission of infection of cattle. Search strings were developed to identify publications from electronic databases to February 2015. Abstracts of 4255 papers identified were reviewed by three reviewers to determine whether the entire article was likely to contain relevant information. Risk factors could be broadly grouped as follows: animal (including nutrition and genetics), herd (including bTB and testing history), environment, wildlife and social factors. Many risk factors are inter-related and study designs often do not enable differentiation between cause and consequence of infection. Despite differences in study design and location, some risk factors are consistently identified, e.g. herd size, bTB history, presence of infected wildlife, whereas the evidence for others is less consistent and coherent, e.g. nutrition, local cattle movements. We have identified knowledge gaps where further research may result in an improved understanding of bTB transmission dynamics. The application of targeted, multifactorial disease control regimens that address a range of risk factors simultaneously is likely to be a key to effective, evidence-informed control strategies.

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