Identifying likely transmissions in Mycobacterium bovis infected populations of cattle and badgers using the Kolmogorov Forward Equations

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.

Deciphering the role of host species for two Mycobacterium bovis genotypes from the European 3 clonal complex circulation within a cattle‐badger‐wild boar multihost system

“…In particular, the role played by each species in these two multi‐host systems remains to be determined. Previous studies on bTB have focused on interactions between two species: primarily cattle and badgers (Biek et al, 2012 ; Bouchez‐Zacria et al, 2018 ; Crispell et al, 2019 ; Rossi et al, 2020 ; Trewby et al, 2014 ) but also cattle and possums (Crispell et al, 2017 ), cattle and elk (Salvador et al, 2019 ), cattle and cervids (Crispell et al, 2020 ), or wild boars and cervids (Zanella et al, 2008 ). However, species such as badgers and wild boars have different life traits: while badgers are sedentary and have a life expectancy of about 14 years, wild boars can travel long distances and are often hunted before they are 4 or 5 years old (Byrne et al, 2014 ; Podgórski et al, 2013 ).…”

Efficient modelling of infectious diseases in wildlife: a case study of bovine tuberculosis in wild badgers

“…Despite extensive research efforts to evaluate risks of bovine tuberculosis (bTB) transmission between cattle and badgers [1][2][3][4][5][6][7][8][9], there remains considerable debate about the relative impacts of within-and between-species spread, and the efficacy of control measures such as badger culling. At Woodchester Park (WP) in Gloucestershire, south-west England, a population of wild badgers has been studied since 1978, providing a unique long-term empirical data set that has provided unprecedented insights into badger ecology, bTB infection in badgers, and transmission to cattle [10][11][12][13][14].…”

“…Despite extensive research efforts to evaluate risks of bTB transmission between cattle and badgers [e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9], there remains considerable debate about the relative impacts of within- and between-species spread, and the efficacy of control measures such as badger culling. In both badger and cattle populations the population-level reproduction number has been inferred to be close to one in both species [1, 6] implying endemic persistence of infection.…”

“…Despite extensive research efforts to evaluate risks of bTB transmission between cattle and badgers (e.g. Cox et al, 2005; Bourne et al, 2007; Donnelly et al, 2007; Biek et al, 2012; Donnelly and Nouvellet, 2013; Hardstaff et al, 2014; Brooks Pollock and Wood, 2015; Rossi et al, 2020; Chang et al, 2023), there remains considerable debate about the relative impacts of within- and between-species spread, and the efficacy of control measures such as badger culling. Common measures for characterising the propensity of disease spread within a population are the basic and effective reproduction numbers (Anderson and May, 1991; Wallinga and Teunis, 2004; Fraser, 2007; Cori et al, 2013).…”

The utility of whole-genome sequencing to identify likely transmission pairs for pathogens with slow and variable evolution

“…WGS of M. bovis has so far been used to characterise clusters of transmission [20][21][22][23][24], and quantify rates of between-species transmission [12,15,20,[25][26][27][28][29][30]. Our interest here is in the more forensic problem of identifying "who-infected-whom" in a population of infected hosts, and whether WGS can be effectively employed to better discriminate between possible animal-to-animal transmission pathways.…”