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

了解抗菌素耐药性（AMR）通过食源性途径传播的风险和方向，并制定干预措施以限制AMR在人类，动物，环境和食品内部和之间的传播是一项重大挑战，需要对人类，动物，环境以及地理，社会和气候相关变量的复杂，相互关联的系统进行360度调查。该项目将开发一个使用人工智能和先进技术的监测系统，以检测AMR在相互关联的人-动物-环境-食品系统（“一个健康”）中的传播。首先，我们将分析历史AMR相关公共数据的异质语料库。这将提高我们对应该收集哪些数据（可监测的生物标志物）以识别导致AMR传播风险更高的条件的理解。这些知识将用于指导大规模多国抽样收集活动，收集来自农场、湿货市场、食品和环境的大量异质和相互关联的数据。数据将包括微生物分析、全基因组测序、宏基因组学、表型分析、农场管理实践文件和环境传感器数据（温度、湿度等）的结果。一个创新的人工智能驱动的数据挖掘管道将用于解开可观察的动物，人类，环境，食品变量与一组核心的耐药基因组，微生物组和微生物基因组学变量之间以前未知的相关性，突出了可在低收入到高收入国家部署的新的监测路线。

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.