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Related Experiment Video

Updated: Sep 26, 2025

Using Generative Art to Convey Past and Future Climate Transitions

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

Predicting diarrhoea outbreaks with climate change.

Tassallah Abdullahi¹, Geoff Nitschke¹, Neville Sweijd²

¹Department of Computer Science, University of Cape Town, Cape Town, Western Cape, South Africa.

|April 19, 2022

Summary

This summary is machine-generated.

Climate change increases diarrhoea outbreaks in South Africa. Machine learning models, especially deep learning, accurately predict daily cases using climate data, aiding early warning systems.

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Area of Science:

Environmental health
Epidemiology
Machine learning applications

Background:

Climate change is projected to worsen diarrhoea outbreaks in developing nations, particularly in South Africa.
Diarrhoeal diseases are a significant cause of illness and death in South Africa.
Accurate prediction of diarrhoea outbreaks is crucial for public health interventions.

Purpose of the Study:

To model the impact of climate change on diarrhoea outbreaks in South Africa.
To predict daily diarrhoea cases using various machine learning methods.
To assess the effectiveness of different machine learning techniques and data augmentation strategies.

Main Methods:

Applied Convolutional Neural Networks (CNNs), Long-Short term Memory Networks (LSTMs), and Support Vector Machines (SVMs).
Utilized climate information and Generative Adversarial Networks (GANs) for data augmentation.
Employed Relevance Estimation and Value Calibration (REVAC) for parameter tuning and sensitivity analysis.

Main Results:

All machine learning methods showed appropriateness for predicting daily diarrhoea cases.
Deep learning methods (CNNs and LSTMs) outperformed SVM.
Data augmentation improved prediction accuracy by at least 30% across all methods.
Precipitation, humidity, evaporation, and temperature were key climate predictors.

Conclusions:

Deep learning methods, particularly CNNs, demonstrated superior prediction accuracy for diarrhoea outbreaks.
The findings support the development of automated early warning systems for diarrhoea and related diseases globally.
Accurate climate-driven predictions are vital for mitigating public health crises.