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

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...

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Autores principales:	K. Stański, S. Lycett, T. Porphyre, B. M. de C. Bronsvoort
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9e2391c871ca4194af962caadc2c9382
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id	oai:doaj.org-article:9e2391c871ca4194af962caadc2c9382
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spelling	oai:doaj.org-article:9e2391c871ca4194af962caadc2c93822021-12-02T10:48:02ZUsing machine learning improves predictions of herd-level bovine tuberculosis breakdowns in Great Britain10.1038/s41598-021-81716-42045-2322https://doaj.org/article/9e2391c871ca4194af962caadc2c93822021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81716-4https://doaj.org/toc/2045-2322Abstract 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.K. StańskiS. LycettT. PorphyreB. M. de C. BronsvoortNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q K. Stański S. Lycett T. Porphyre B. M. de C. Bronsvoort Using machine learning improves predictions of herd-level bovine tuberculosis breakdowns in Great Britain
description	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.
format	article
author	K. Stański S. Lycett T. Porphyre B. M. de C. Bronsvoort
author_facet	K. Stański S. Lycett T. Porphyre B. M. de C. Bronsvoort
author_sort	K. Stański
title	Using machine learning improves predictions of herd-level bovine tuberculosis breakdowns in Great Britain
title_short	Using machine learning improves predictions of herd-level bovine tuberculosis breakdowns in Great Britain
title_full	Using machine learning improves predictions of herd-level bovine tuberculosis breakdowns in Great Britain
title_fullStr	Using machine learning improves predictions of herd-level bovine tuberculosis breakdowns in Great Britain
title_full_unstemmed	Using machine learning improves predictions of herd-level bovine tuberculosis breakdowns in Great Britain
title_sort	using machine learning improves predictions of herd-level bovine tuberculosis breakdowns in great britain
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9e2391c871ca4194af962caadc2c9382
work_keys_str_mv	AT kstanski usingmachinelearningimprovespredictionsofherdlevelbovinetuberculosisbreakdownsingreatbritain AT slycett usingmachinelearningimprovespredictionsofherdlevelbovinetuberculosisbreakdownsingreatbritain AT tporphyre usingmachinelearningimprovespredictionsofherdlevelbovinetuberculosisbreakdownsingreatbritain AT bmdecbronsvoort usingmachinelearningimprovespredictionsofherdlevelbovinetuberculosisbreakdownsingreatbritain
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Using machine learning improves predictions of herd-level bovine tuberculosis breakdowns in Great Britain

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