Page 49 - SAMRC AnnualReport 2025-26

P E R FOR M A NC E I N FOR M A T I ON

markers that provide early warning signals of public Berlin, Germany, in October 2025. The engagement
health threats at a population level. supports ongoing efforts to expand the use of AI
and data-driven technologies within the Unit and
Building on this platform, EHRU scientists developed across collaborative research platforms.
an artificial intelligence (AI)-driven predictive model
that integrates clinical case data with wastewater
SARS-CoV-2 signals. The model forecasts clinical
case trends by assessing whether changes in viral
concentrations in wastewater can predict future
waves of infection. By combining environmental
surveillance with AI-based modelling, the Unit is
strengthening proactive, data-informed public
health decision-making and enhancing pandemic
preparedness.
This scalable approach demonstrates the value
of integrating environmental, clinical, and
epidemiological data streams and has potential
applications for monitoring other pathogens and
public health threats, including antimicrobial EHRU, together with the Biomedical Research
resistance. In recognition of this work, EHRU and Innovation Platform and the Genomics
Director Dr. Renee Street was invited to participate Platform, co-leads the SAMRC Wastewater
in a World Health Organization workshop on AI Surveillance and Research Programme.
and automation for epidemiological pipelines in

EHRU research on uranium exposure among children living near gold mining tailings facilities in Johannesburg
found elevated uranium levels in children’s hair samples compared to those from non-mining areas.

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