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...
| Autores principales: | , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
MDPI
2024
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/158392 |
Ejemplares similares: An XGBoost approach to predictive modelling of Rift Valley fever outbreaks in Kenya using climatic factors
- Machine learning approach to predicting Rift Valley fever disease outbreaks in Kenya
- A systematic literature review with meta-analysis of predictive modelling of Rift Valley fever outbreaks in East Africa: Machine learning and time series approaches
- Impact of Rift Valley fever outbreaks on food price index in Burundi: An interrupted time series analysis
- Forecasting and modelling of Rift Valley fever outbreaks using Autoregressive Integrated Moving Average (ARIMA) models: Evaluating the impact of 2018 and 2021 Rift Valley fever outbreaks on Kenyan food price index
- Anomalous precipitation as a factor for the spatio-temporal distribution of Rift Valley fever in Uganda
- Ecological factors associated with abundance and distribution of mosquito vectors of Rift Valley fever virus during an epidemic period in Isiolo, Kenya