ArticleScientific reports2021
Using machine learning improves predictions of herd-level bovine tuberculosis breakdowns in Great Britain.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Machine learning augmented diagnostic testing to identify sources of variability in test performance.PLoS computational biology · 2025Article
- Field-oriented assessment of bovine tuberculosis in Tunisian cattle: IDR, PCRWorld journal of microbiology & biotechnology · 2025Article
- Disease Prediction in Cattle: A Mixed-Methods Review of Predictive Modeling Studies.Animals : an open access journal from MDPI · 2025Review
- [Predictive indicators and risk model construction for coronary artery lesions in Kawasaki disease children over 5 years old].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2024Article
- Comparison of Machine Learning Tree-Based Algorithms to Predict Future Paratuberculosis ELISA Results Using Repeat Milk Tests.Animals : an open access journal from MDPI · 2024Article
- The Irish bTB eradication programme: combining stakeholder engagement and research-driven policy to tackle bovine tuberculosis.Irish veterinary journal · 2023Review
- Will we ever eradicate animal tuberculosis?Irish veterinary journal · 2023Article
- Article
- Platelet and ferritin as early predictive factors for the development of macrophage activation syndrome in children with Kawasaki disease: A retrospective case-control study.Frontiers in pediatrics · 2023Article
- Future Risk of Bovine Tuberculosis (Microorganisms · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
In the United Kingdom, despite decades of control efforts, bovine tuberculosis (bTB) has not been controlled and currently costs ~ £100 m annually. Critical in the failure of control efforts has been the lack of a sufficiently sensitive diagnostic test. Here we use machine learning (ML) to predict herd-level bTB breakdowns in Great Britain (GB) with the aim of improving herd-level diagnostic sensitivity. The results of routinely-collected herd-level tests were correlated with risk factor data. Four ML methods were independently trained with data from 2012-2014 including ~ 4700 positive herd-level test results annually. The best model's performance was compared to the observed sensitivity and specificity of the herd-level test calculated on the 2015 data resulting in an increased herd-level sensitivity from 61.3 to 67.6% (95% confidence interval (CI): 66.4-68.8%) and herd-level specificity from 90.5 to 92.3% (95% CI: 91.6-93.1%). This approach can improve predictive capability for herd-level bTB and support disease control.
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