US-UK Collab: Mycobacterial Transmission Dynamics in Agricultural Systems: Integrating Phylogenetics, Epidemiology, Ecology, and Economics

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Mathematical modeling of disease transmission is an important tool in studying infectious disease control; however, parameter estimation from field data is often difficult to quantify, especially with complex diseases. Whole-genome sequencing has become faster and more affordable in recent years, thanks to Next-Generation Sequencing techniques. As large databases of sequenced pathogen isolates become available, infectious disease epidemiologists will be able to use these databases, which are more fine-grained than previous serotyping methods, to improve our understanding of transmission dynamics. However, a general methodology is not yet available for such detailed data in bacteria with slow transmission timescales. This project proposes to develop a quantitative methodology for incorporating whole genome sequence (WGS) data into bacterial transmission models. For this, isolates will be used from mycobacterial infections in agricultural systems in the US and UK. Mycobacterial infections, including Mycobacterium avium subsp. paratuberculosis (MAP) and M. bovis (bovine tuberculosis or bTB), are usually studied in simple one-host systems. Mycobacterial disease is extremely difficult to control due to long latent periods, poor diagnostic sensitivity, wildlife and environmental reservoirs of infection, and heterogeneous strain infectiousness. The key to controlling these diseases is believed to be an integrated approach to understand the pathways through which pathogen transmission occurs at all levels in an ecosystem: within animals, between individual animals, between livestock and wildlife, and between livestock and the environment. A generalized, multi-scale methodology is proposed for determining transmission dynamics using WGS data and phylodynamics. These dynamics will be incorporated into predictive models of disease transmission and control within an economic framework, to help decision-makers make informed control choices.