AI Article Synopsis
- Bovine tuberculosis (bTB) in the UK remains poorly controlled despite significant spending, costing around £100 million per year.
- Researchers applied machine learning techniques to enhance the sensitivity of diagnostic tests for bTB, using data from 2012-2014 that included about 4,700 positive test results annually.
- The best machine learning model improved the detection capabilities, increasing sensitivity from 61.3% to 67.6% and specificity from 90.5% to 92.3%, demonstrating potential for better disease control in cattle herds.
Article 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.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7838174 | PMC |
| http://dx.doi.org/10.1038/s41598-021-81716-4 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!