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

Joseph Crispell, Clare H. Benton, Daniel Balaz, Nicola De Maio, Assel Ahkmetova, Adrian Allen, Roman Biek, Eleanor L. Presho, James Dale, Glyn Hewinson, Samantha J. Lycett, Javier Nunez-Garcia, Robin A. Skuce, Hannah Trewby, Daniel J. Wilson, Ruth N. Zadoks, Richard J. Delahay*, Rowland Raymond Kao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

50 Citations (SciVal)
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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.

Original languageEnglish
Article numbere45833
Number of pages36
JournaleLife
Volume8
DOIs
Publication statusPublished - 17 Dec 2019

Keywords

  • Animals
  • Animals, Wild/microbiology
  • Bayes Theorem
  • Cattle
  • Disease Reservoirs/microbiology
  • Genome, Bacterial/genetics
  • Genomics/methods
  • Host-Pathogen Interactions
  • Mustelidae/microbiology
  • Mycobacterium bovis/classification
  • Phylogeny
  • Tuberculosis, Bovine/epidemiology

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