Study Sites

Sampling will be conducted at two wastewater treatment plants in Grand Tunis. Both untreated and treated wastewater will be collected monthly. These sites are important because treated wastewater is partly reused and partly discharged into the environment.

Priority Targets

The project will monitor priority resistant bacteria, including resistant Enterobacterales, Acinetobacter baumannii, Salmonella, Shigella, Staphylococcus aureus, Enterococcus faecium, and Pseudomonas aeruginosa.

Key AMR genes will include blaCTX-M, blaNDM, blaKPC, blaOXA-48, mecA, vanA, mcr-1, qnr, tet, sul, dfrA, erm(B), and intI1.

AI Platform

TAWA will integrate laboratory results with wastewater flow, population equivalent, physicochemical parameters, weather, seasonality, treatment-plant characteristics, industrial activity, geographical data, and antibiotic sales.

AI models will predict AMR trends and potential hotspots. A dashboard will display contamination patterns and may later support email or SMS alerts.

Expected Impact

TAWA will:

• Complement clinical AMR surveillance.
• Enable earlier detection of emerging resistance.
• Support targeted public health interventions.
• Generate evidence for national AMR policies.
• Strengthen Tunisia’s surveillance and laboratory capacity.

Partner Roles

• Institut Pasteur de Tunis: Scientific coordination, microbiological and molecular analyses, sequencing, bioinformatics, and AI modelling.
• Ministry of Health – DHMPE: Sample transport, historical surveillance data, and environmental health support.
• ONAS: Wastewater-flow data, technical support, and authorization of sampling.

Conclusion

TAWA will establish an AI-enabled wastewater surveillance platform to strengthen AMR prevention, detection, and response in Tunisia.