Researchers at the United Arab Emirates University (UAEU), in collaboration with the Indian Institute of Technology Madras Zanzibar campus, have developed a data-driven framework to model malaria transmission dynamics. This is reported by Emirates News Agency (WAM), a partner of TV BRICS.
The study presents a method that incorporates temperature- and altitude-dependent variables into compartmental disease models to improve prediction accuracy.
The research team, led by Adithya Rajnarayanan, Manoj Kumar, and Abdessamad Tridane, applied artificial intelligence tools, including artificial neural networks (ANNs), recurrent neural networks (RNNs), and physics-informed neural networks (PINNs).
Professor Tridane stated that embedding environmental dependencies into transmission functions allows the model to capture the complex behaviour of malaria spread and provides a timely method for disease tracking.
The study targets improved forecasting in regions such as sub-Saharan Africa, which accounts for 94 per cent of global malaria cases.
Photo: iStock /Â Supatman
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