Using machine learning improves predictions of herd-level bovine tuberculosis breakdowns in Great Britain

Kajetan Stanski, Samantha Lycett, Thibaud Porphyre, Mark Bronsvoort

Research output: Contribution to journalArticlepeer-review

Abstract

In the United Kingdom, despite decades of control efforts, bovine tuberculosis (bTB) has not been controlled and currently costs ~£100m 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 ~4,700 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.
Original languageEnglish
JournalScientific Reports
DOIs
Publication statusPublished - 26 Jan 2021

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