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Article Dans Une Revue Scientific Reports Année : 2021

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

Résumé

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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Dates et versions

hal-03747513 , version 1 (14-06-2024)

Identifiants

Citer

K. Stański, S. Lycett, Thibaud Porphyre, Barend M. de C. Bronsvoort. Using machine learning improves predictions of herd-level bovine tuberculosis breakdowns in Great Britain. Scientific Reports, 2021, 11 (1), pp.2208. ⟨10.1038/s41598-021-81716-4⟩. ⟨hal-03747513⟩
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