Non-typhoidal Salmonella (NTS) infections are a major public health concern, causing an estimated 150 million cases and 60,000 deaths worldwide each year. NTS serovars Newport, Dublin, and Cerro are common causes of infection in cattle but are also found to occur in humans. There is also pervasive multi-drug resistance in NTS serovars due to the widespread usage of antibiotics in the animal production industry. It is therefore important to study between-host transmission rates and the mobility of Antimicrobial Resistance (AMR) genes within these serovars. The overall objective of this project is to use Bayesian phylodynamic modelling to estimate the transmission rates of Salmonella Newport, Dublin, Cerro and other NTS serovars between humans and cattle, and to estimate how frequently AMR genes or plasmids are gained or lost between animals and humans. We will be using publicly available data from the databases NCBI-pathogens and Enterobase, where collectively there are ~200,000 sequenced NTS genomes. These data will be combined and filtered based on sequence quality (genome completeness and contamination) and metadata quality (known source of isolation (host), date of isolation, and location). We will also be using antibiogram data from NCBI Biosample Antibiograms, where the MICs for at least 18 different antibiotics are available for tens to thousands of strains of each serovar. This antibiogram data can be used to select isolates having phenotypic resistance in tandem with genotypic or predicted resistance. All genomes will be processed through the Bactopia analysis pipeline for initial quality filtering, genome assembly and annotation. Pairwise ANI calculations and clustering will be performed using skani. Within-lineage variant calling will be performed using snippy and maximum likelihood phylogenies will be constructed using IQ-TREE2. AMR gene detection will be performed using abriTAMR. We will use a Bayesian phylodynamic approach to estimate the transmission rates and the rates at which AMR genes are gained or lost between humans and cattle with models built for BEAST2. These models will allow us to incorporate temporal, geographic and other genetic/phenotypic metadata to infer and compare epidemiological parameters for resistant lineages vs susceptible lineages and/or human vs cattle lineages. The findings of this project will have a significant impact on our understanding of the epidemiology of NTS serovars. This information can be used to develop more effective control and prevention strategies for these infections.