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

This project aims to develop new AI-powered surveillance solutions to identify increased risk of AMR emergence and direction of spread through the food-borne route based on the 'One Health' concept, and capable to detect the appearance of known and novel AMR traits. The solutions will be suitable for deployment in low-to-high income countries. While there have been many studies on AMR in livestock, environment, and human, they often focus on a specific sector and/or rely on a specific analysis (antimicrobial usage or whole genome sequencing) but not necessarily on integrated data analysis. Our goal is to strengthen big data approaches to integrate surveillance across human, animal, environment with the food chain to assist interventions to prevent AMR caused by resistant enteric bacterial pathogens. We plan to devise a real-time monitoring method capable of pinpointing geographical locations and routes which -at any point in time- may be at higher risk of developing AMR. This will be achieved by a triangulated approach: i) Understand the conditions leading to higher risk of AMR, by developing a cloud-solution embedding auto-adaptive learning to integrate and mine public data from different sources (metagenomics, phenotypes, satellite, etc) at different scales (region/setting/country, etc) and subjects (humans, animals etc); ii) Perform an AI-guided experimental sampling collection campaign of unprecedented scale and coverage; iii) Identify monitorable biomarkers indicating increased risk of AMR and direction of spread, embedded in deployable surveillance solutions.