Abstract: Computational zoonoses: Multi-disciplinary insights from animal tuberculosis data

Abstract: Computational zoonoses: Multi-disciplinary insights from animal tuberculosis data

Computational zoonoses – application of computational methods and tools to study the transmission dynamics of zoonotic diseases - is crucial for enhancing our understanding of zoonotic diseases and supporting the development of effective control and prevention strategies. The use of computational approaches in this field enables researchers to better understand the dynamical systems underlying epidemiology, transmission, genetic evolution, and ecology of zoonotic pathogens. For example, animal tuberculosis (TB) is a disease that impacts multiple host-species, including humans, and can persist in environmental reservoirs such as soil, making its study inherently difficult. Our work will demonstrate how incorporating data from various fields, such as genomics, ecology, epidemiology, and computational modeling enables a more comprehensive and accurate representation of TB transmission dynamics across host-species and environments.