FightAMR: Novel global One Health surveillance approach to fight AMR using Artificial Intelligence and big data mining

Understanding the risk and direction of antimicrobial resistance (AMR) spread through food-borne routes, and developing of interventions to limit the spread of AMR within and between humans, animals, environment and food is a significant challenge, requiring a 360-degree investigation of a complex, interconnected system of humans, animals, environment on one hand and geographical, societal and climate-related variables on the other. This project will develop a monitoring system using AI and advanced tech to detect AMR spread in the interconnected human-animal-environment-food system ('One Health'). First, we will analyse the heterogeneous corpus of historical AMR-related public data. This will improve our understanding of what data (monitorable biomarkers) should be collected to identify the conditions leading to a higher risk of AMR spread. This knowledge will be used to guide a large-scale multi-country sampling collection campaign of a large amount of heterogeneous and interconnected data from farms, wet markets, food, environment. Data will include results of microbiological analysis, whole-genome sequencing, metagenomics, phenotyping, documentation of on-farm management practices, and environmental sensor data (temperature, humidity, etc). An innovative AI-powered data mining pipeline will be used to unravel previously unknown correlations between observable animal, human, environment, food variables and a core set of resistome, microbiome, and microbial genomics variables, highlighting new routes for surveillance deployable in low-to-high-income countries.