Predicting diarrhoea outbreak with climate change

dc.contributor.advisorNitschke, Geoff
dc.contributor.authorAbdullahi, Tassallah Amina
dc.date.accessioned2021-07-13T10:43:23Z
dc.date.available2021-07-13T10:43:23Z
dc.date.issued2021
dc.date.updated2021-07-13T10:35:36Z
dc.description.abstractClimate change is expected to exacerbate diarrhoea outbreak in South Africa, a leading cause of morbidity and mortality in the region. In this study, we modelled the impacts of climate change on diarrhoea with machine learning methods. We applied two deep learning techniques, convolutional neural networks (CNNs) and long-short term memory networks (LSTMs); and a support vector machine to predict daily diarrhoea cases over the different South African provinces by incorporating climate information. Generative Adversarial Networks (GANs) was used to generate synthetic data which was used to augment the available dataset. Furthermore, relevance estimation and value calibration (REVAC) was used to tune the parameters of the machine learning algorithms to optimize the accuracy of their predictions. Sensitivity analysis was also performed to investigate the contribution of the different climate factors to the diarrhoea prediction model. The results of the study showed that all three ML methods were appropriate for predicting daily diarrhoea cases with respect to the selected climate variables in each South African province. The ML methods were all able to yield low and similar RMSE. However, the level of accuracy for each model varied across different experiments, with the deep learning models outperforming the SVM model. Among the deep learning techniques, the CNN model performed best when only real-world dataset was used, while the LSTM model outperformed the other models when the real dataset was augmented with synthetic data. Across the provinces, the accuracy of all three ML algorithms improved by at least 30% when data augmentation was implemented. In addition, REVAC improved the accuracy of the CNN model by more than 12% in KwaZulu Natal province. However, the percentage increase in accuracy of the LSTM model was less than 4% in Western Cape province when REVAC was used. Our sensitivity analysis revealed that the most influential climate variables to be considered when predicting outbreak of diarrhoea in South Africa are precipitation, humidity, evaporation and temperature conditions. The result of this study is important for the development of an early warning system for diarrhoea outbreak over South Africa.
dc.identifier.apacitationAbdullahi, T. A. (2021). <i>Predicting diarrhoea outbreak with climate change</i>. (). ,Faculty of Science ,Department of Computer Science. Retrieved from http://hdl.handle.net/11427/33615en_ZA
dc.identifier.chicagocitationAbdullahi, Tassallah Amina. <i>"Predicting diarrhoea outbreak with climate change."</i> ., ,Faculty of Science ,Department of Computer Science, 2021. http://hdl.handle.net/11427/33615en_ZA
dc.identifier.citationAbdullahi, T.A. 2021. Predicting diarrhoea outbreak with climate change. . ,Faculty of Science ,Department of Computer Science. http://hdl.handle.net/11427/33615en_ZA
dc.identifier.ris TY - Master Thesis AU - Abdullahi, Tassallah Amina AB - Climate change is expected to exacerbate diarrhoea outbreak in South Africa, a leading cause of morbidity and mortality in the region. In this study, we modelled the impacts of climate change on diarrhoea with machine learning methods. We applied two deep learning techniques, convolutional neural networks (CNNs) and long-short term memory networks (LSTMs); and a support vector machine to predict daily diarrhoea cases over the different South African provinces by incorporating climate information. Generative Adversarial Networks (GANs) was used to generate synthetic data which was used to augment the available dataset. Furthermore, relevance estimation and value calibration (REVAC) was used to tune the parameters of the machine learning algorithms to optimize the accuracy of their predictions. Sensitivity analysis was also performed to investigate the contribution of the different climate factors to the diarrhoea prediction model. The results of the study showed that all three ML methods were appropriate for predicting daily diarrhoea cases with respect to the selected climate variables in each South African province. The ML methods were all able to yield low and similar RMSE. However, the level of accuracy for each model varied across different experiments, with the deep learning models outperforming the SVM model. Among the deep learning techniques, the CNN model performed best when only real-world dataset was used, while the LSTM model outperformed the other models when the real dataset was augmented with synthetic data. Across the provinces, the accuracy of all three ML algorithms improved by at least 30% when data augmentation was implemented. In addition, REVAC improved the accuracy of the CNN model by more than 12% in KwaZulu Natal province. However, the percentage increase in accuracy of the LSTM model was less than 4% in Western Cape province when REVAC was used. Our sensitivity analysis revealed that the most influential climate variables to be considered when predicting outbreak of diarrhoea in South Africa are precipitation, humidity, evaporation and temperature conditions. The result of this study is important for the development of an early warning system for diarrhoea outbreak over South Africa. DA - 2021_ DB - OpenUCT DP - University of Cape Town KW - Computer Science LK - https://open.uct.ac.za PY - 2021 T1 - Predicting diarrhoea outbreak with climate change TI - Predicting diarrhoea outbreak with climate change UR - http://hdl.handle.net/11427/33615 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/33615
dc.identifier.vancouvercitationAbdullahi TA. Predicting diarrhoea outbreak with climate change. []. ,Faculty of Science ,Department of Computer Science, 2021 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/33615en_ZA
dc.language.rfc3066eng
dc.publisher.departmentDepartment of Computer Science
dc.publisher.facultyFaculty of Science
dc.subjectComputer Science
dc.titlePredicting diarrhoea outbreak with climate change
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationlevelMSc
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