A Systematic Literature Review with Meta-Analysis of Predictive Modelling of Rift Valley Fever Outbreaks in East Africa: Machine Learning and Time Series Approaches
DOI:
https://doi.org/10.51483/IJAIML.5.1.2025.1-11Keywords:
Machine learning, Time series, Outbreak, Inclusion criteria, PICOS, PRISMAAbstract
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.
