Abstract
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.
Highlights
An important distinction exists between contact network and transmission network: while the former includes all potential transmission contacts, the latter is a subset of the former describing pathogen transmission patterns[5,6]
We follow the approach taken by Sharkey[33], who used the Kolmogorov Forward Equations (KFEs) to describe the infection dynamics at the individual and pairwise level, but here we add states to describe the evolution of the pathogen
Following an earlier phylogenetic analysis by Crispell et al.[36], the infected population of cattle and badgers was divided into five clades according to their genetic distance
Summary
An important distinction exists between contact network and transmission network: while the former includes all potential transmission contacts, the latter is a subset of the former describing pathogen transmission patterns[5,6]. In combination with an increasing ability to extract genetic material (either directly from clinical samples or from cultured isolates) and with rapid and minimal processing, large-scale characterization of populations of pathogen genomes is possible[8,9,10]. These advances have proven to be transformative for forensic epidemiology, especially when populations can be densely sampled. The observed genome diversity in a population of pathogens is the result of processes happening at two different scales: the evolution of the pathogen’s genome within the host and its transmission to another h ost[11]. This would not provide any information about the direction of transmission[11]
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