Researchers from UAEU and Indian Institute of Technology have built an AI‑powered framework to accurately predict malaria outbreaks.
AL AIN: Researchers at the United Arab Emirates University (UAEU), in partnership with the Indian Institute of Technology Madras Zanzibar campus, have unveiled a pioneering framework for malaria outbreak prediction, blending artificial intelligence with mathematical epidemiology. This breakthrough aims to improve public health readiness in malaria-prone regions.
The collaborative study, now published in Scientific Reports by Nature, introduces a model that integrates temperature- and altitude-sensitive parameters directly into disease transmission equations. By using AI tools such as artificial neural networks (ANNs), recurrent neural networks (RNNs), and physics-informed neural networks (PINNs), the framework delivers more accurate forecasting of malaria hotspots.
The team’s approach—described as a global first—enables the simulation of malaria spread in real-time, helping public health officials pre-empt outbreaks and manage resources more effectively. A standout feature of the model is its use of Dynamic Mode Decomposition (DMD), which provides an immediate infection risk metric based on environmental factors.
The AI malaria prediction model was developed by Adithya Rajnarayanan, Manoj Kumar, and Prof. Abdessamad Tridane of UAEU, who believes the study addresses a critical gap in global disease forecasting.
“This research demonstrates the power of AI when combined with classical epidemiological models,” said Prof. Tridane. “By embedding environmental dependencies directly into the transmission functions, our model captures the complex, real-world behaviour of malaria spread.”
With over 600,000 deaths attributed to malaria annually, mostly in sub-Saharan Africa, the AI malaria prediction model offers a data-backed tool to guide public health policy and early interventions, especially as climate patterns continue to shift.
