Mini-Grant Award: A Prediction Model for Malaria Outbreaks within a Climate-driven multi-hazard early warning system for South Africa

Climate change is increasingly linked to rising infectious disease outbreaks worldwide. Leveraging Artificial Intelligence (AI) and big data analysis of medical and climate data enables early detection and prediction of climate-related infectious diseases. Combining Indigenous Knowledge Systems (IKS) with AI and big data can enhance resilience and provide solutions for combating outbreaks.

The proposed project seeks to develop an intelligent community-based system that integrates Indigenous knowledge (IK) and Machine Learning (ML) to predict the outbreak of malaria using big climate data (climate and earth observation data) and historical outbreaks of malaria. Expected outcomes include:

  • A generic integrated and intelligent early warning system framework.
  • Machine-based algorithm(s) modelling correlations between infectious diseases and climatic variations.
  • Prototype of an intelligent early warning system for predicting malaria outbreaks.
  • A tool that provides stakeholders with a new perspective on predicting infectious disease outbreaks related to extreme climate variations.
  • Enables proactive planning, targeted interventions, efficient resource allocation, and response strategies.


  1. Professor Ondego Joel, University of KwaZulu-Natal
  2. Dr Adeyinka Akanbi, Central University of Technology
  3. Paulina Phoobane, Central University of Technology

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