PreprintReviewVersion 1This version is not peer-reviewed
A Systematic Literature Review with Meta – Analysis of Predictive Modelling of Rift Valley Fever Outbreaks in East Africa: Machine Learning and Time Series Approaches
Version 1
: Received: 12 September 2024 / Approved: 12 September 2024 / Online: 14 September 2024 (05:11:33 CEST)
How to cite:
Mulwa, D. F.; Kazuzuru, B.; Misinzo, G.; Bett, B. A Systematic Literature Review with Meta – Analysis of Predictive Modelling of Rift Valley Fever Outbreaks in East Africa: Machine Learning and Time Series Approaches. Preprints2024, 2024091014. https://doi.org/10.20944/preprints202409.1014.v1
Mulwa, D. F.; Kazuzuru, B.; Misinzo, G.; Bett, B. A Systematic Literature Review with Meta – Analysis of Predictive Modelling of Rift Valley Fever Outbreaks in East Africa: Machine Learning and Time Series Approaches. Preprints 2024, 2024091014. https://doi.org/10.20944/preprints202409.1014.v1
Mulwa, D. F.; Kazuzuru, B.; Misinzo, G.; Bett, B. A Systematic Literature Review with Meta – Analysis of Predictive Modelling of Rift Valley Fever Outbreaks in East Africa: Machine Learning and Time Series Approaches. Preprints2024, 2024091014. https://doi.org/10.20944/preprints202409.1014.v1
APA Style
Mulwa, D. F., Kazuzuru, B., Misinzo, G., & Bett, B. (2024). A Systematic Literature Review with Meta – Analysis of Predictive Modelling of Rift Valley Fever Outbreaks in East Africa: Machine Learning and Time Series Approaches. Preprints. https://doi.org/10.20944/preprints202409.1014.v1
Chicago/Turabian Style
Mulwa, D. F., Gerald Misinzo and Benard Bett. 2024 "A Systematic Literature Review with Meta – Analysis of Predictive Modelling of Rift Valley Fever Outbreaks in East Africa: Machine Learning and Time Series Approaches" Preprints. https://doi.org/10.20944/preprints202409.1014.v1
Abstract
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 multi-disciplinary models that incorporate climatic condition, geographical, biological, and social factors.
Keywords
machine learning; time series; outbreak; inclusion criteria; PICOS; PRISMA
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
