Mycobacterial transmission dynamics in agricultural systems: integrating phylogenetics, epidemiology, ecology and economics


With the collaborative USDA-NIFA-AFRI and BBSRC funding and using the US-UK science partnership, we are able to investigate mycobacterial transmission in agricultural systems.

Mycobacterium avium subsp. paratuberculosis (MAP) and M. bovis (bovine tuberculosis or bTB) are economically important pathogens in animal and human health. Mycobacterial diseases caused by these pathogens are 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.

The proposal seeks to identify evolutionary factors affecting the ecology and transmission of important slow transmission mycobacteria. A novel modelling strategy will be developed incorporating whole genome sequencing in to bacterial transmission models. The innovation includes a study of how economic choices made by ranchers/farmers influence the evolutionary history of Tb in cattle.

The project takes advantage of data which is already available as well as collecting new data in a longitudinal manner in the United States (US) and the United Kingdom (UK). A proposed compartmental model will be utilized to evaluate the economics of control options/interventions such as culling, wildlife mitigation, etc. Cattle herd and wildlife data will be used to validate the model. The proposed integration methodology and modelling system will be invaluable for expanding the understanding of the ecology of infectious diseases.

Adaptation of the model from developed cattle industries to livestock holders in low-income countries would allow for modelling of disease spread in more resource-constrained environments; having developed the necessary techniques in well-defined systems, less data would be necessary for model adaptation than for model creation, and model validation and testing will allow us to identify the minimum data requirements. Such a model would be a tool in poverty alleviation among livestock holders.

First published: 12 September 2014

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