An Intelligent Prediction Tool for Infectious Diseases’ Outbreaks Using Machine Learning, Climate Data and Indigenous Knowledge

DOI

10.2991/978-94-6239-705-7_7How to use a DOI?

Keywords

Infectious disease prediction; Machine learning; Climate change; Indigenous Knowledge (IK); Early warning system; Climate data

Abstract

Substantial evidence suggests that climate change significantly contributes to the global rise in infectious disease outbreaks. Advances in Artificial Intelligence (AI) and big climate data have enhanced the accuracy of medical and climate analysis, supporting accurate prediction of climate-related diseases. Conversely, Indigenous Knowledge (IK) has proven its worth in strengthening community resilience to the effects of climate change. When integrated with AI and climate data, IK has the potential to contribute to solutions for combating outbreaks of infectious diseases. Even though AI has already been conjoined with IK to tackle one of the complex problems brought by climate change, drought, this amalgamation has not been explored in predicting infectious diseases. Consequently, this paper proposes a model that integrates IK and machine learning to predict the outbreak of infectious diseases using climate data and malaria historical outbreaks. The resulting model is evaluated using an early warning system prototype to equip key stakeholders with a decision support tool. Implemented using data from Vhembe District in South Africa, the study demonstrates that this approach offers high predictive accuracy (up to 93%) and is culturally relevant, illustrating how coupling IK with modern science can lead to relevant and effective prediction systems for the local people that such systems are intended to serve.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Paulina Phoobane
AU  - Muthoni Masinde
PY  - 2026
DA  - 2026/06/13
TI  - An Intelligent Prediction Tool for Infectious Diseases’ Outbreaks Using Machine Learning, Climate Data and Indigenous Knowledge
BT  - Proceedings of the FIREtalk Conference - Research on FIRE! (research-on-fire 2025)
PB  - Atlantis Press
SP  - 86
EP  - 103
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-94-6239-705-7_7
DO  - 10.2991/978-94-6239-705-7_7
ID  - Phoobane2026
ER  -