Machine learning approach to predicting Rift Valley fever disease outbreaks in Kenya
In Kenya, Rift Valley fever (RVF) outbreaks pose significant challenges, being one of the most severe climate-sensitive zoonoses. While machine learning (ML) techniques have shown superior performance in time series forecasting, their application in predicting disease outbreaks in Africa remains und...
| Main Authors: | , , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
MDPI
2025
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/175702 |
Similar Items: Machine learning approach to predicting Rift Valley fever disease outbreaks in Kenya
- An XGBoost approach to predictive modelling of Rift Valley fever outbreaks in Kenya using climatic factors
- 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
- Is Rift Valley fever virus a threat for Germany? Evaluation of the vector competence of German mosquitoes for Rift Valley fever virus
- Anomalous precipitation as a factor for the spatio-temporal distribution of Rift Valley fever in Uganda