Abdullahi, Tassallah and Nitschke, Geoff and Sweijd, Neville (2022) Predicting diarrhoea outbreaks with climate change, PLOS One, e0262008, 17, PLOS.
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Abstract
Climate change is expected to exacerbate diarrhoea outbreaks across the developing world, most notably in Sub-Saharan countries such as South Africa. In South Africa, diseases related to diarrhoea outbreak is a leading cause of morbidity and mortality. In this study, we modelled the impacts of climate change on diarrhoea with various machine learning (ML) methods to predict daily outbreak of diarrhoea cases in nine South African provinces.
| Item Type: | Journal article (paginated) |
|---|---|
| Subjects: | Computing methodologies > Artificial intelligence Computing methodologies > Machine learning Computing methodologies > Modeling and simulation Computing methodologies > Modeling and simulation > Model development and analysis |
| Date Deposited: | 23 Sep 2022 07:47 |
| Last Modified: | 23 Sep 2022 07:47 |
| URI: | https://pubs.cs.uct.ac.za/id/eprint/1539 |
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