This project aims to develop new AI-powered surveillance solutions to identify increased risk of AMR emergence and direction of spread through the foodborne 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. 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 may be at higher risk of developing AMR. This will be achieved by

1. 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, at different scales and compartments;
2. Perform an AI-guided experimental sampling collection campaign of unprecedented scale and coverage;
3. Identify monitorable biomarkers indicating increased risk of AMR and direction of spread, embedded in deployable surveillance solutions.