Antimicrobial resistance (AMR) poses a profound threat to global health, challenging our ability to treat infections in humans, animals, and the environment. Our interest in this project is driven by the urgent need to develop innovative approaches that can predict and mitigate the spread of AMR. By leveraging the extensive ATLAS dataset, which includes over 917,049 antibiotics, this project aims to integrate genetic data with phylogenetic analysis to map the evolutionary trajectories of resistant bacteria. The project will utilize tools, such as MEGA and BEAST, to construct detailed phylogenetic trees, thereby uncovering the relationships between bacterial genealogy and resistance mechanisms. Additionally, predictive models employing machine learning techniques will be developed to forecast the emergence and spread of resistant strains within the One Health framework. These models will incorporate genetic data, resistance trends, and indicators across human, animal, and environmental health sectors, providing a comprehensive understanding of AMR dynamics. This research is not only scientifically compelling but also critically important for informing targeted interventions and policy decisions. The insights gained from this study will contribute to the development of strategies that address AMR holistically, safeguarding public health and ensuring the continued efficacy of antibiotics. Our commitment to this project stems from a deep-seated belief in the potential of interdisciplinary research to tackle complex global challenges like AMR. We are confident that our findings will make a significant impact on the field and contribute to the global effort to combat this pressing issue.
Center for Global Research Data