Operating across 100 strategic testing sites, the RASID platform has processed over 75,000 samples to track more than 400 biological and chemical indicators
ABU DHABI — September 4, 2026 : Health and municipal authorities in Abu Dhabi are using an artificial intelligence surveillance platform to monitor the emirate’s wastewater network, enabling officials to identify biological and chemical risks days before clinical symptoms emerge in the general population.
The digital surveillance network, named RASID, aggregates biological readings gathered from 100 monitoring facilities across the emirate. By pairing continuous molecular testing with predictive machine learning models, the system gives health regulators population-level visibility into community illness trends, hazardous discharges, and environmental shifts.
Global health tech group M42 presented the mechanics of the platform publicly during the Abu Dhabi International Hunting and Equestrian Exhibition (ADIHEX 2026), running through September 6 at the ADNEC Centre.
Developed in direct partnership with the Abu Dhabi Quality and Conformity Council (ADQCC), the surveillance programme has been functioning continuously since 2022. During this period, the Central Testing Laboratory has screened upwards of 75,000 wastewater samples drawn from sites across the country, evaluating each against more than 400 biological and chemical parameters.
Unlike traditional health reporting, which relies on individuals seeking treatment at clinics or hospitals, population-level sewage tracking identifies viral loads and toxic chemicals anonymously at the community level. The platform processes these high-volume datasets to assign risk scores to specific geographical zones, allowing civil defense, environmental authorities, and healthcare providers to allocate resources before localized spikes turn into wider emergencies.
Dimitris Moulavasilis, Group Chief Executive Officer of M42, stated that analyzing underlying environmental markers produces actionable operational intelligence that supports swift, data-driven preventative measures.
Abdulla Hassan Al Muaini, Executive Director of the Central Testing Laboratory at ADQCC, noted that combining routine testing routines with machine learning models has built one of the Middle East’s largest biological and environmental surveillance datasets, strengthening both biosecurity and urban health management.
