Bovine tuberculosis (bTB) continues to be the costliest, most complex animal health problem in England. The effectiveness of the test-and-slaughter policy is hampered by the imperfect sensitivity of the surveillance tests. Up to half of recurrent incidents within 24 months of a previous one could have been due to undetected infected cattle not being removed. Improving diagnostic testing with more sensitive tests, like the interferon (IFN)-gamma test, is one of the government's top priorities. However, blanket deployment of such tests could result in more false positive results (due to imperfect specificity), together with logistical and cost-efficiency challenges. A targeted application of such tests in higher prevalence scenarios, such as a subpopulation of high-risk herds, could mitigate against these challenges. We developed classification machine learning algorithms (using 80% of 2012-2019 bTB surveillance data as the training set) to evaluate the deployment of IFN-gamma testing in high-risk herds (i.e. those at risk of an incident in England) in two testing data sets: i) the remaining 20% of 2012-19 data, and ii) 2020 bTB surveillance data. The resulting model, classification tree analysis, with an area under a receiver operating characteristic (ROC) curve (AUC) > 95, showed a 73% sensitivity and a 97% specificity in the 2012-2019 test dataset. Used on 2020 data, it predicted eight percent (3 510 of 41 493) of eligible active herds as at-risk of a bTB incident, the majority of them (66% or 2 328 herds) experiencing at least one. Whilst all predicted at-risk herds could have preventive measures applied, the additional application of IFN-gamma test in parallel interpretation to the statutory skin test, if the risk materialises, would have resulted in 8 585 additional IFN-gamma reactors detected (a 217% increase over the 2 710 IFN-gamma reactors already detected by tests carried out). Only 18% (330 of 1 819) of incidents in predicted high-risk herds had the IFN-gamma test applied in 2020. We therefore conclude that this methodology provides a better way of directing the application of the IFN-gamma test towards the high-risk subgroup of herds. Classification tree analysis ensured the systematic identification of high-risk herds to consistently apply additional measures in a targeted way. This could increase the detection of infected cattle more efficiently, preventing recurrence and accelerating efforts to achieve eradication by 2038. This methodology has wider application, like targeting improved biosecurity measures in avian influenza at-risk farms to limit damage to the industry in future outbreaks.
Keywords: Bovine tuberculosis; Classification tree analysis; England; Incident management; Machine learning.
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