Page 100 - SAMRC AnnualReport 2025-26

Group has contributed significantly to developing
the next generation of malaria scientists by
providing opportunities for advanced research,
technical skills development, and participation
in regional and international collaborations. This
investment strengthens local scientific leadership
and research sustainability.

The annual Southern Africa Malaria Research
Conference also serves as a key platform for
inclusive knowledge exchange, bringing together
researchers, policymakers, healthcare professionals,
and students to foster collaboration, professional
development, and translation of research findings
into public health interventions.

The MRG further contributes to regional capacity
strengthening through initiatives such as the
Lubombo Spatial Development Initiative and cross-
border malaria control programmes in Mozambique.
These efforts have informed national vector
control policies, strengthened malaria prevention
strategies, and supported equitable regional
collaboration to reduce malaria transmission among
vulnerable populations.

Overall, the MRG’s activities integrate scientific
capacity-building, policy-relevant research, regional
partnerships, and equitable knowledge-sharing,
contributing meaningfully to the empowerment,
dignity, and health of malaria-affected communities.

AI in Medical and Health Research

The MRG is increasingly integrating advanced
modelling, climate analytics, and data-driven
technologies into malaria research to improve
strategic decision-making and disease forecasting.
Through the CAMMISA Consortium, the Unit
applies mathematical and statistical modelling
alongside climate science approaches to better
understand how climate variability and long-
term climate change affect malaria transmission
patterns. This work includes generating future
climate and disease transmission scenarios over
short-, medium-, and long-term timeframes,
enabling more accurate forecasting of malaria risks
and supporting targeted intervention planning
across Southern Africa.
These modelling approaches also provide a strong
foundation for future AI-supported predictive
analytics by generating large, high-quality datasets Community engagement and blood spot
and sophisticated modelling frameworks. in KwaZulu-Natal.

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