Researchers from United Arab Emirates University (UAEU) and IIT Madras’s Zanzibar campus have introduced a data-driven framework that models and forecasts malaria transmission. Moreover, the approach integrates artificial intelligence with mathematical modelling to support early intervention and improved disease control.

A region-specific modelling approach

Specifically, the study presents a compartmental model that includes temperature- and altitude-dependent variables. Consequently, simulations better reflect local environmental influences on malaria spread. This makes forecasts more realistic for climate-sensitive and vulnerable areas.

AI and dynamic systems

Additionally, the team used Artificial Neural Networks, Recurrent Neural Networks, and Physics-Informed Neural Networks to improve prediction accuracy. Furthermore, they applied Dynamic Mode Decomposition to break complex dynamics into simpler components. As a result, the researchers derived a near real-time infection risk metric for early detection.

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Implications and collaboration

The model aims to strengthen surveillance and guide targeted responses in high-burden regions. Therefore, it can inform early warning systems and data-driven policymaking. The work reflects collaboration between UAEU and IIT Madras’s Zanzibar campus, with potential benefits for regional public health.