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: | , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
SvedbergOpen
2025
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/175635 |
| _version_ | 1855531231215616000 |
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| author | Mulwa, D.F. Kazuzuru, B. Misinzo, G. Bett, Bernard K. |
| author_browse | Bett, Bernard K. Kazuzuru, B. Misinzo, G. Mulwa, D.F. |
| author_facet | Mulwa, D.F. Kazuzuru, B. Misinzo, G. Bett, Bernard K. |
| author_sort | Mulwa, D.F. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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 of climatic changes on RVF outbreaks published between 1930 and 2024. The literature search involved databases including PubMed, PLOS ONE, JSTOR, Web of Science, Google Scholar, and SCOPUS (Kenmoe et al., 2023). The results show that most of the articles were published between 2018 and 2022, and most of the articles were from United States, France, and Kenya. We conducted a detailed review of the articles using the PRISMA 2020 flow chart, screening and qualifying 10,015 articles. Some articles revealed significant gaps in both internal and external validation. Therefore, future research should focus on developing multidisciplinary models that incorporate climatic condition, geographical, biological, and social factors. |
| format | Journal Article |
| id | CGSpace175635 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | SvedbergOpen |
| publisherStr | SvedbergOpen |
| record_format | dspace |
| spelling | CGSpace1756352025-07-15T05:07:51Z A systematic literature review with meta-analysis of predictive modelling of Rift Valley fever outbreaks in East Africa: Machine learning and time series approaches Mulwa, D.F. Kazuzuru, B. Misinzo, G. Bett, Bernard K. machine learning modelling rift valley fever time series analysis zoonoses 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 of climatic changes on RVF outbreaks published between 1930 and 2024. The literature search involved databases including PubMed, PLOS ONE, JSTOR, Web of Science, Google Scholar, and SCOPUS (Kenmoe et al., 2023). The results show that most of the articles were published between 2018 and 2022, and most of the articles were from United States, France, and Kenya. We conducted a detailed review of the articles using the PRISMA 2020 flow chart, screening and qualifying 10,015 articles. Some articles revealed significant gaps in both internal and external validation. Therefore, future research should focus on developing multidisciplinary models that incorporate climatic condition, geographical, biological, and social factors. 2025-01-25 2025-07-15T04:35:19Z 2025-07-15T04:35:19Z Journal Article https://hdl.handle.net/10568/175635 en Open Access SvedbergOpen Mulwa, D.F., Kazuzuru, B., Misinzo, G. and Bett, B. 2025. A systematic literature review with meta-analysis of predictive modelling of Rift Valley fever outbreaks in East Africa: Machine learning and time series approaches. International Journal of Artificial Intelligence and Machine Learning 5(1): 1–11. |
| spellingShingle | machine learning modelling rift valley fever time series analysis zoonoses Mulwa, D.F. Kazuzuru, B. Misinzo, G. Bett, Bernard K. A systematic literature review with meta-analysis of predictive modelling of Rift Valley fever outbreaks in East Africa: Machine learning and time series approaches |
| title | A systematic literature review with meta-analysis of predictive modelling of Rift Valley fever outbreaks in East Africa: Machine learning and time series approaches |
| title_full | A systematic literature review with meta-analysis of predictive modelling of Rift Valley fever outbreaks in East Africa: Machine learning and time series approaches |
| title_fullStr | A systematic literature review with meta-analysis of predictive modelling of Rift Valley fever outbreaks in East Africa: Machine learning and time series approaches |
| title_full_unstemmed | A systematic literature review with meta-analysis of predictive modelling of Rift Valley fever outbreaks in East Africa: Machine learning and time series approaches |
| title_short | A systematic literature review with meta-analysis of predictive modelling of Rift Valley fever outbreaks in East Africa: Machine learning and time series approaches |
| title_sort | systematic literature review with meta analysis of predictive modelling of rift valley fever outbreaks in east africa machine learning and time series approaches |
| topic | machine learning modelling rift valley fever time series analysis zoonoses |
| url | https://hdl.handle.net/10568/175635 |
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