An XGBoost approach to predictive modelling of Rift Valley fever outbreaks in Kenya using climatic factors
Reports of Rift Valley fever (RVF), a highly climate-sensitive zoonotic disease, have been rather frequent in Kenya. Although multiple empirical analyses have shown that machine learning methods outperform time series models in forecasting time series data, there is limited evidence of their applica...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
MDPI
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/158392 |
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