Kao, Biek, Herzyk

Quantifying the transmission dynamics of multi-host pathogens is challenging, especially when sampling is biased. An important example of this is Mycobacterium bovis, the causative agent of bovine TB (bTB) an important disease of British and Irish cattle. M. bovis circulates in the badger and cattle populations and is currently expanding rapidly in GB.

While it is well known that persistence of bTB in cattle is spatially localised, the critical question of how these spatial 'patches' are spread and maintained and the role badgers play are as yet unresolved questions. Here, we propose to combine whole genome sequencing (WGS) technology with detailed population data on both hosts to shed new light onto this problem. To address this question, we shall use the exceptional datasets available to integrate analyses across two organisational scales - the 'patch' scale of local persistence and spread and the transmission at the individual level within cattle herds and badger social groups.

First, the natural spatial scale at which M. bovis is circulating shall be explored, using community structure algorithms from social network analysis to parse densely sampled phylogenetic trees by the most relevant clade structures.

Second, the persistence and spread of bTB at the cattle herd and badger social group scale shall be investigated by analysing detailed life history and infection data. Mathematical models will be used to infer parameter distributions using Bayesian approaches; using methods that have already been used to study a number of important infectious diseases, including bTB.

As the integration of genetic and epidemiological data ('phylodynamics') is as yet little applied to bacterial pathogens, not only shall this project generate important insights into bTB epidemiology, it shall also be a useful exemplar to others interested in mycobacterial phylodynamics in general.

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First published: 3 July 2014

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