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Elife. 2019 Dec 17;8. pii: e45833. doi: 10.7554/eLife.45833.

Combining genomics and epidemiology to analyse bi-directional transmission of Mycobacterium bovis in a multi-host system.

Author information

1
School of Veterinary Medicine, Veterinary Sciences Centre, University College Dublin, Dublin, Ireland.
2
National Wildlife Management Centre, Animal & Plant Health Agency (APHA), London, United Kingdom.
3
Roslin Institute, University of Edinburgh, Edinburgh, United Kingdom.
4
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom.
5
Institute of Biodiversity, Animal Health & Comparative Medicine, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, United Kingdom.
6
Agri-Food & Biosciences Institute Northern Ireland (AFBNI), Belfast, United Kingdom.
7
Animal & Plant Health Agency (APHA), London, United Kingdom.
8
Centre for Bovine Tuberculosis, Institute of Biological, Environmental and Rural Sciences, University of Aberystwyth, Aberystwyth, United Kingdom.
9
Genomics Medicine Ireland, Dublin, Ireland.
10
Quadram Institute Bioscience, Norwich, United Kingdom.
11
Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
12
Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom.

Abstract

Quantifying pathogen transmission in multi-host systems is difficult, as exemplified in bovine tuberculosis (bTB) systems, but is crucial for control. The agent of bTB, Mycobacterium bovis, persists in cattle populations worldwide, often where potential wildlife reservoirs exist. However, the relative contribution of different host species to bTB persistence is generally unknown. In Britain, the role of badgers in infection persistence in cattle is highly contentious, despite decades of research and control efforts. We applied Bayesian phylogenetic and machine-learning approaches to bacterial genome data to quantify the roles of badgers and cattle in M. bovis infection dynamics in the presence of data biases. Our results suggest that transmission occurs more frequently from badgers to cattle than vice versa (10.4x in the most likely model) and that within-species transmission occurs at higher rates than between-species transmission for both. If representative, our results suggest that control operations should target both cattle and badgers.

KEYWORDS:

Mycobacterium bovis; badger; bovine tuberculosis; cattle; epidemiology; global health; infectious disease; microbiology; whole genome sequencing

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