UAEU and IIT Madras Zanzibar has developed AI-based structure to forecast malaria outbreaks
In an important step for global public health, researchers at the United Arab Emirates University (UAEU) and researchers at the Indian Institute of Technology Madras have introduced a state-of-the-art, data-powered structure that accurately models and predicts malaria transmission. By integrating artificial intelligence with mathematical modeling, this new approach aims to support initial intervention and improve disease control strategies in malaria-prone areas.
A new approach to malaria modeling
A collaborative research team led by Adithya Rajnarayanan, Manoj Kumar, and Professor Abdisamad Tadiidan has introduced a novel method that increases how the outbreak of malaria can be predicted. Their work is published Scientific report By nature, presents a comprehensive model that combines artificial intelligence (AI) with classical epidemiology to simulate the dynamics of malaria with high precision. Study, titled “Analysis of a mathematical model for malaria using data-powered approach”The disease brings a new approach to modeling. This compartment covers temperature- and height-dependent variables in the disease model, a method that makes simulation more realistic and region-specific. This is particularly important for climate-sensitive and weak areas where environmental factors heavy affect malaria transmission patterns.
Technologies in Core: AI and Dynamic Systems
To promote the future capacity of its model, researchers appointed a suit of advanced AI devices. These include:
- Artificial nerve network (ANNs)
- Recurrent nerve network (RNN)
- Physics-informed nerve network (PINNS)
Each of these devices was used to improve the accuracy of the disease forecast, enabling the model to detect patterns in a complex intercourse between environmental conditions and malaria proliferation. Additionally, the study introduced dynamic mode decomposition (DMD), a mathematical technique, which helps to break the complex systems into simple, understandable components. It was used to create a real -time transition risk metric, providing a powerful resource to public health authorities for early detection and targeted response.
Implication for global health
UAEU Professor Abhyasamad Tridain emphasized the importance of this integration of AI with epidemic science, which states:This research displays the power of AI when combined with the model of classical epidemiology, “Prof. Abhusamad Triden of UAAU said.” By embedding environmental dependence directly into transmission tasks, our model captures the complex, real-world behavior of the spread of malaria, which provides more accurate and timely method to monitor the disease. ,The implications of this research are particularly relevant to regions such as sub-city Africa, responsible for 94% of global malaria cases. Each year with deaths related to more than half million malaria, the need for precise forecast models is important. This work offers a valuable step towards better monitoring, initial warning system and data-operated policy-making in the fight against malaria.
Institutional cooperation and background
This study represents a collaboration between two institutions that are expanding their global health research footprints:
- The United Arab Emirates University (UAEU) is the oldest public research university in the UAE, established in 1976 in Al AN. Established by Sheikh Zayed bin Sultan Al Nahyan, it offers a wide range of graduates and postgraduate programs in many subjects.
- Inaugurated in November 2023, the IIT Madras Zanzibar Campus, Indian Institute of Technology is the first international campus of Madras. Located in the Ballio district of Zanzibar, Tanzania, the complex currently offers programs in data science and artificial intelligence. It aims to fulfill a diverse student population from India, Tanzania and other African countries with a plan to broaden its educational scope in the coming years.
