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Predicting diarrhoea outbreak with climate change
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 ne...
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| Format: | Thesis |
| Language: | English |
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Department of Computer Science
2021
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| _version_ | 1867613157566971904 |
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| access_status_str | Open Access |
| author | Abdullahi, Tassallah Amina |
| author2 | Nitschke, Geoff |
| author_browse | Abdullahi, Tassallah Amina Nitschke, Geoff |
| author_facet | Nitschke, Geoff Abdullahi, Tassallah Amina |
| author_sort | Abdullahi, Tassallah Amina |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/33615 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:31:41.113Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Department of Computer Science |
| publisherStr | Department of Computer Science |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/33615 Predicting diarrhoea outbreak with climate change Abdullahi, Tassallah Amina Nitschke, Geoff Computer Science 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. 2021-07-13T10:43:23Z 2021-07-13T10:43:23Z 2021 2021-07-13T10:35:36Z Master Thesis Masters MSc http://hdl.handle.net/11427/33615 eng application/pdf Department of Computer Science Faculty of Science |
| spellingShingle | Computer Science Abdullahi, Tassallah Amina Predicting diarrhoea outbreak with climate change |
| thesis_degree_str | Master's |
| title | Predicting diarrhoea outbreak with climate change |
| title_full | Predicting diarrhoea outbreak with climate change |
| title_fullStr | Predicting diarrhoea outbreak with climate change |
| title_full_unstemmed | Predicting diarrhoea outbreak with climate change |
| title_short | Predicting diarrhoea outbreak with climate change |
| title_sort | predicting diarrhoea outbreak with climate change |
| topic | Computer Science |
| url | http://hdl.handle.net/11427/33615 |
| work_keys_str_mv | AT abdullahitassallahamina predictingdiarrhoeaoutbreakwithclimatechange |