Bovine tuberculosis (Mycobacterium bovis) is a disease of cattle with severe consequences for agriculture in the British Isles. The Eurasian badger (Meles meles) is implicated in the spread and maintenance of bovine tuberculosis in the cattle population and various measures have been trialed in badgers to control infection. A five-year pilot Test, Vaccinate and Remove investigation (TVR) was carried out in a 100km 2 area of Northern Ireland that tested, vaccinated, and removed infected badgers. This study used machine learning techniques in order to predict whether a badger has bovine tuberculosis using data collected from the TVR study. Several machine learning models – Decision Trees, Random Forests, Logistic Regression, XGBoost – were created and attempted in order to classify the data with the highest accuracy. Synthetic Minority Oversampling Technique (SMOTE) was also carried out due to imbalance in the data. The C5.0 decision tree model was chosen as the final model. This model was the most appropriate choice as it achieved a very high AUC score with a value of 0.974 in training and 0. 962 in testing. It also had the benefit of being a white-box model. Almost all of the variables were found to be significant, including the visual diagnostic tests used in the study, thus supporting their importance. The final model gives confidence in current diagnostic tests to accurately identify infected badgers and helps to inform future diagnostic test regimes. This study represents one of the first applications of machine learning in wildlife disease control.
In the British Isles, it is generally accepted that the Eurasian badger (Meles meles) plays a role in the maintenance of bovine tuberculosis (bTB) in cattle. Non-selective culling is the main intervention method deployed in controlling bTB in badgers along with smaller scale Bacillus Calmette-Guérin (BCG) vaccination areas. This paper describes the use of selective badger culling combined with vaccination in a research intervention trial.In Northern Ireland, a 100 km2 area was subjected to a test and vaccinate or remove (TVR) badger intervention over a 5-year period. Badgers were individually identified and tested on an annual basis. Physical characteristics and clinical samples were obtained from each unique badger capture event.A total of 824 badgers were trapped with 1520 capture/sampling events. There were no cage-related injuries to the majority of badgers (97%). A low level of badger removal was required (4.1%-16.4% annually), while 1412 BCG vaccinations were administered. A statistically significant downward trend in the proportion of test positive badgers was observed.This is the first project to clearly demonstrate the feasibility of cage side testing of badgers. The results provide valuable data on the logistics and resources required to undertake a TVR approach to control Mycobacterium bovis in badgers.
A novel five year Test and Vaccinate or Remove (TVR) wildlife research intervention project in badgers ( Meles meles) commenced in 2014 in a 100km 2 area of Northern Ireland. It aimed to increase the evidence base around badgers and bovine TB and help create well-informed and evidence-based strategies to address the issue of cattle-to-cattle spread and spread between cattle and badgers. It involved real-time trap-side testing of captured badgers and vaccinating those that tested negative for bTB (BadgerBCG–BCG Danish 1331) and removal of those that tested bTB positive using the Dual-Path Platform VetTB test (DPP) for cervids (Chembio Diagnostic Systems, Medford, NY USA). Four diagnostic tests were utilised within the study interferon gamma release assay (IGRA), culture (clinical samples and post mortem), DPP using both whole blood and DPP using serum. BCG Sofia (SL222) was used in the final two years because of supply issues with BadgerBCG. Objectives for this study were to evaluate the performance of the DPP in field conditions and whether any trend was apparent in infection prevalence over the study period. A Bayesian latent class model of diagnostic test evaluation in the absence of a gold standard was applied to the data. Temporal variation in the sensitivity of DPP and interferon gamma release assay (IGRA) due to the impact of control measures was investigated using logistic regression and individual variability was assessed. Bayesian latent class analysis estimated DPP with serum to have a sensitivity of 0.58 (95% CrI: 0.40–0.76) and specificity of 0.97 (95% CrI: 0.95–0.98). The DPP with whole blood showed a higher sensitivity (0.69 (95% CrI: 0.48–0.88)) but similar specificity (0.98 (95% Crl: 0.96–0.99)). The change from BCG Danish to BCG Sofia significantly impacted on DPP serum test characteristics. In addition, there was weak evidence of increasing sensitivity of IGRA over time and differences in DPP test sensitivity between adults and cubs. An exponential decline model was an appropriate representation of the infection prevalence over the 5 years, with a starting prevalence of 14% (95% CrI: 0.10–0.20), and an annual reduction of 39.1% (95% CrI: 26.5–50.9). The resulting estimate of infection prevalence in year 5 of the study was 1.9% (95% CrI: 0.8–3.8). These results provide field evidence of a statistically significant reduction in badger TB prevalence supporting a TVR approach to badger intervention. They give confidence in the reliability and reproducibility in the DPP Whole Blood as a real time trap-side diagnostic test for badgers, and describe the effect of vaccination and reduced infection prevalence on test characteristics.
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