The infectome of NSW dairy calves, a genomic microbial surveillance . Infectious diseases are the main cause of disease and mortality in calves. The knowledge of the diversity of infectious disease-causing agents in NSW dairy cattle is not comprehensive. Thus, the immediate goal of this proposal is to redress this knowledge gap using untargeted microbial genomic sequencing to characterise and identify known and emerging enteric and respiratory pathogens in dairy calves. We will determine the occ ....The infectome of NSW dairy calves, a genomic microbial surveillance . Infectious diseases are the main cause of disease and mortality in calves. The knowledge of the diversity of infectious disease-causing agents in NSW dairy cattle is not comprehensive. Thus, the immediate goal of this proposal is to redress this knowledge gap using untargeted microbial genomic sequencing to characterise and identify known and emerging enteric and respiratory pathogens in dairy calves. We will determine the occurrence and distribution of their microbial species across all NSW dairy regions. This will enable the Australian dairy industry to improve animal health and productivity, and diagnostic capacity, which will allow farmers to make informed management decisions about disease control strategies. Read moreRead less
Data-driven phylodynamics: molecular evolution to epidemiology. This project aims to uncover how different environmental and ecological variables drive the emergence of pathogens with increased transmissibility or virulence, known as variants. This will be achieved through extensive analyses of virus genome data.
This project expects to generate new knowledge in the field of pathogen evolution using novel data-driven statistical techniques for genomic analyses.
Expected outcomes of this proje ....Data-driven phylodynamics: molecular evolution to epidemiology. This project aims to uncover how different environmental and ecological variables drive the emergence of pathogens with increased transmissibility or virulence, known as variants. This will be achieved through extensive analyses of virus genome data.
This project expects to generate new knowledge in the field of pathogen evolution using novel data-driven statistical techniques for genomic analyses.
Expected outcomes of this project are a new understanding of the circumstances under which pathogen variants emerge and a suite of statistical tools to exploit the vast genome data available.
This should provide significant benefits by generating new knowledge with the potential to improve biosecurity, agriculture, and heath.
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