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Established methods for whole-genome-sequencing (WGS) technology allow for the detection of single-nucleotide polymorphisms (SNPs) in the pathogen genomes sourced from host samples. The information obtained can be used to track the pathogen's evolution in time and potentially identify 'who-infected-whom' with unprecedented accuracy. Successful methods include 'phylodynamic approaches' that integrate evolutionary and epidemiological data. However, they are typically computationally intensive, require extensive data, and are best applied when there is a strong molecular clock signal and substantial pathogen diversity. To determine how much transmission information can be inferred when pathogen genetic diversity is low and metadata limited, we propose an analytical approach that combines pathogen WGS data and sampling times from infected hosts. It accounts for 'between-scale' processes, in particular within-host pathogen evolution and between-host transmission. We applied this to a well-characterised population with an endemic Mycobacterium bovis (the causative agent of bovine/zoonotic tuberculosis, bTB) infection. Our results show that, even with such limited data and low diversity, the computation of the transmission probability between host pairs can help discriminate between likely and unlikely infection pathways and therefore help to identify potential transmission networks. However, the method can be sensitive to assumptions about within-host evolution.

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., Competing Interests: JC, CB, DB, ND, AA, AA, RB, EP, JD, GH, SL, JN, RS, HT, DW, RZ, RD, RK No competing interests declared, (© 2019, Crispell et al.)

Background: Bovine tuberculosis (bTB), caused by Mycobacterium bovis, is an important livestock disease raising public health and economic concerns around the world. In New Zealand, a number of wildlife species are implicated in the spread and persistence of bTB in cattle populations, most notably the brushtail possum (Trichosurus vulpecula). Whole Genome Sequenced (WGS) M. bovis isolates sourced from infected cattle and wildlife across New Zealand were analysed. Bayesian phylogenetic analyses were conducted to estimate the substitution rate of the sampled population and investigate the role of wildlife. In addition, the utility of WGS was examined with a view to these methods being incorporated into routine bTB surveillance., Results: A high rate of exchange was evident between the sampled wildlife and cattle populations but directional estimates of inter-species transmission were sensitive to the sampling strategy employed. A relatively high substitution rate was estimated, this, in combination with a strong spatial signature and a good agreement to previous typing methods, acts to endorse WGS as a typing tool., Conclusions: In agreement with the current knowledge of bTB in New Zealand, transmission of M. bovis between cattle and wildlife was evident. Without direction, these estimates are less informative but taken in conjunction with the low prevalence of bTB in New Zealand's cattle population it is likely that, currently, wildlife populations are acting as the main bTB reservoir. Wildlife should therefore continue to be targeted if bTB is to be eradicated from New Zealand. WGS will be a considerable aid to bTB eradication by greatly improving the discriminatory power of molecular typing data. The substitution rates estimated here will be an important part of epidemiological investigations using WGS data.

Mycobacterium bovis is the causal agent of bovine tuberculosis, one of the most important diseases currently facing the UK cattle industry. Here, we use high-density whole genome sequencing (WGS) in a defined sub-population of M. bovis in 145 cattle across 66 herd breakdowns to gain insights into local spread and persistence. We show that despite low divergence among isolates, WGS can in principle expose contributions of under-sampled host populations to M. bovis transmission. However, we demonstrate that in our data such a signal is due to molecular type switching, which had been previously undocumented for M. bovis. Isolates from farms with a known history of direct cattle movement between them did not show a statistical signal of higher genetic similarity. Despite an overall signal of genetic isolation by distance, genetic distances also showed no apparent relationship with spatial distance among affected farms over distances <5 km. Using simulations, we find that even over the brief evolutionary timescale covered by our data, Bayesian phylogeographic approaches are feasible. Applying such approaches showed that M. bovis dispersal in this system is heterogeneous but slow overall, averaging 2 km/year. These results confirm that widespread application of WGS to M. bovis will bring novel and important insights into the dynamics of M. bovis spread and persistence, but that the current questions most pertinent to control will be best addressed using approaches that more directly integrate WGS with additional epidemiological data., (Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.)

Understanding how emergent pathogens successfully establish themselves and persist in previously unaffected populations is a crucial problem in disease ecology, with important implications for disease management. In multi‐host pathogen systems this problem is particularly difficult, as the importance of each host species to transmission is often poorly characterised, and the disease epidemiology is complex. Opportunities to observe and analyse such emergent scenarios are few.Here, we exploit a unique dataset combining densely collected data on the epidemiological and evolutionary characteristics of an outbreak of Mycobacterium bovis (the causative agent of bovine tuberculosis, bTB) in a population of cattle and badgers in an area considered low risk for bTB, with no previous record of either persistent infection in cattle, or of any infection in wildlife. We analyse the outbreak dynamics using a combination of mathematical modelling, Bayesian evolutionary analyses and machine learning.Comparison to M. bovis whole‐genome sequences from Northern Ireland confirmed this to be a pathogen single introduction from the latter region, with evolutionary analysis supporting an introduction directly into the local cattle population 6 years prior to its first discovery in badgers.Once introduced, the evidence supports M. bovis epidemiological dynamics passing through two phases, the first dominated by cattle‐to‐cattle transmission before becoming established in the local badger population.Synthesis and applications. The Mycobacterium bovis emergent outbreak that was the object of this study was of considerable concern because of the geographical distance from previously known high‐risk areas. Initial decisions about the outbreak control were supported by the whole‐genome sequencing data. The further analyses described here were used to estimate the time of introduction (and therefore the likely magnitude of any hidden outbreak) and the rates of cross‐species transmission, and provided valuable confirmation that the extent and focus of the imposed controls were appropriate. Not only do these findings strengthen the call for genomic surveillance, but they also pave the path for future outbreaks control, providing insights for more rapid and decisive evidence‐based decision‐making. As the methods we used and developed are agnostic to the disease itself, they are also valuable for other slowly transmitting pathogens. [ABSTRACT FROM AUTHOR]