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、动物、环境和食物内部和之间的传播是一项重大挑战，需要对人、动物、环境以及与地理、社会和气候有关的变量组成的复杂、相互关联的系统进行360度调查。本项目将利用人工智能和先进技术开发一个监测系统，以检测AMR在相互关联的人-动物-环境-食物系统(One Health)中的传播。首先，我们将分析与AMR相关的历史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)以确定导致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活动，收集来自农场、菜市场、食品和环境的大量不同种类和相互关联的数据。数据将包括微生物分析、全基因组测序、元基因组学、表型分析、农场管理做法的记录以及环境传感器数据(温度、湿度等)的结果。一条由人工智能支持的创新数据挖掘管道将用于揭示可观察到的动物、人类、环境、食品变量与一组抵抗组、微生物组和微生物基因组学变量之间以前未知的相关性，从而突出可在低收入到高收入国家部署的监测的新途径。

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.