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.
Highlights
Control of a pathogen in a system where it can infect multiple species requires an understanding of the role of each host species in the infection dynamics (Haydon et al, 2002)
When each host species is capable of maintaining infection independently, control operations in one species can be rendered ineffective as a result of spillover from another
On the islands of Britain and Ireland, the current evidence suggests that effective control of infection in cattle is hindered by transmission from an infected wildlife population – the European badger (Meles meles) (Godfray et al, 2013)
Summary
Control of a pathogen in a system where it can infect multiple species requires an understanding of the role of each host species in the infection dynamics (Haydon et al, 2002). Evidence to date suggests that, even with access to pathogen sequence data, obtaining directional estimates of transmission might only be possible at the population level and will require dense targeted sampling and fine-grained epidemiological metadata (Kao et al, 2016; Kao et al, 2014), as has previously been demonstrated in investigations of M. tuberculosis outbreaks in humans (Bryant et al, 2013; Gardy et al, 2011; Guthrie et al, 2018; Walker et al, 2012; Walker et al, 2018; Yang et al, 2017) and in tracing between cattle herds for outbreaks of M. bovis (Biek et al, 2012; Salvador et al, 2019). These approaches have yet to be applied to situations where dense multi-host pathogen data are available
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