Imagine plastic trash in the ocean acting like a floating raft that helps superbugs grow and travel. This project studies how warming waters and plastic waste make these drug-resistant bacteria spread faster. It's like mapping a highway for germs to find better ways to stop them from reaching people and animals.
Companies and cities struggle to quantify how plastic waste and rising temperatures accelerate the spread of drug-resistant bacteria. This creates unmanaged health risks and regulatory uncertainty in water management.
Simulation-based decision supporting tools, machine learning predictive models, and molecular tracking tools for water-borne AMR.
If you are a wastewater provider dealing with rising antibiotic-resistant bacteria in effluent — this project developed simulation-based decision supporting tools that help predict risk zones and implement nature-based filters to protect local water sources.
If you are a consultancy dealing with corporate plastic footprint audits — this project developed indicators and predictive capacity using machine learning that quantify how plastic pollution contributes to health risks in aquatic environments.
If you are a health authority dealing with unknown sources of drug-resistant infections — this project developed molecular tools to track how bacteria move from plastic in water back into land-based systems.
Based on available project data, no specific pricing or commercial cost for the tools is mentioned; the project is funded by a EUR 6,283,534 EU contribution.
The project aims to translate findings from Italy and the Philippines to a pan-European scale using spatio-temporal modelling and machine learning.
Based on available project data, there are no specific details regarding patents or licensing agreements provided in the project summary.
The project focuses on science-policy translation to guide policies regarding antimicrobial resistance and plastic pollution, potentially impacting future environmental health regulations.
The project period runs from 2024-01-01 to 2028-06-30, suggesting that final tools and indicators will be ready by mid-2028.
The consortium is heavily weighted toward research and academia, with 4 universities and 5 research organizations. Industrial presence is low at 9% (1 company), indicating the project is primarily driven by scientific discovery and policy influence rather than immediate commercial product development. The geographic spread across 7 countries, including the Philippines, suggests a focus on diverse environmental data collection.
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