A systematic literature review with meta-analysis of predictive modelling of Rift Valley fever outbreaks in East Africa: Machine learning and time series approaches
Rift Valley fever (RVF), is a viral zoonotic disease predominant in East Africa and transmitted by Aedes mosquitoes carrying the virus. Using the systematic literature review approach, the present study evaluated machine learning techniques and time series approaches to find literature on the impact...
| Main Authors: | , , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
SvedbergOpen
2025
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/175635 |
Similar Items: A systematic literature review with meta-analysis of predictive modelling of Rift Valley fever outbreaks in East Africa: Machine learning and time series approaches
- 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
- 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
- Epidemiological assessment of the Rift Valley fever outbreak in Kenya and Tanzania in 2006 and 2007
- Genomic epidemiology of Rift Valley fever virus involved in the 2018 and 2022 outbreaks in livestock in Rwanda