Researchers at the United Arab Emirates University (UAEU), in collaboration with the Indian Institute of Technology Madras Zanzibar campus, have unveiled a pioneering data-driven framework for modelling malaria transmission dynamics.
Published in the prestigious journal Scientific Reports by Nature, the study represents a significant advancement in global health modelling, combining artificial intelligence (AI) with classical mathematical epidemiology.
The paper, titled “Analysis of a Mathematical Model for Malaria Using a Data-Driven Approach”, introduces a novel method for predicting malaria outbreaks by integrating temperature- and altitude-dependent variables into compartmental disease models. This enables more realistic simulations of malaria transmission, particularly in vulnerable, climate-sensitive regions.
Led by Adithya Rajnarayanan, Manoj Kumar, and Prof. Abdessamad Tridane, the research team employed advanced AI tools—including artificial neural networks (ANNs), recurrent neural networks (RNNs), and physics-informed neural networks (PINNs)—to significantly improve prediction accuracy.
The study also incorporates Dynamic Mode Decomposition (DMD) to generate a real-time infection risk metric, providing public health authorities with a powerful tool for early intervention and strategic resource planning.
“This research demonstrates the power of AI when combined with classical epidemiological models,” said Prof. Abdessamad Tridane of UAEU. “By embedding environmental dependencies directly into transmission functions, our model captures the complex, real-world behaviour of malaria spread—providing a more accurate and timely method for disease tracking.”
The study addresses the urgent global need for enhanced infectious disease forecasting, particularly in regions such as sub-Saharan Africa, which accounts for 94 percent of malaria cases worldwide. With over half a million malaria-related deaths reported annually, this research lays the groundwork for future studies and informed policies aimed at combating one of the world’s most persistent public health challenges.
–Input WAM
