A systematic literature review with meta-analysis of predictive modelling of Rift Valley fever outbreaks in East Africa: Machine learning and time series approaches

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cg.authorship.typesCGIAR and developing country institute
cg.contributor.affiliationSokoine University of Agriculture
cg.contributor.affiliationInternational Livestock Research Institute
cg.coverage.regionAfrica
cg.coverage.regionEastern Africa
cg.creator.identifierBernard Bett: 0000-0001-9376-2941
cg.howPublishedFormally Published
cg.identifier.dataurlhttps://www.kaggle.com/datasets/damarisfelistusmulwa/rift-valley-fever-data-from-1981-to-2010-kenya
cg.identifier.doihttps://doi.org/10.51483/ijaiml.5.1.2025.1-11
cg.issn2789-2557
cg.issue1
cg.journalInternational Journal of Artificial Intelligence and Machine Learning
cg.reviewStatusPeer Review
cg.subject.ilriRVF
cg.subject.ilriZOONOTIC DISEASES
cg.subject.impactAreaNutrition, health and food security
cg.subject.sdgSDG 2 - Zero hunger
cg.volume5
dc.contributor.authorMulwa, D.F.
dc.contributor.authorKazuzuru, B.
dc.contributor.authorMisinzo, G.
dc.contributor.authorBett, Bernard K.
dc.date.accessioned2025-07-15T04:35:19Z
dc.date.available2025-07-15T04:35:19Z
dc.identifier.urihttps://hdl.handle.net/10568/175635
dc.titleA systematic literature review with meta-analysis of predictive modelling of Rift Valley fever outbreaks in East Africa: Machine learning and time series approachesen
dcterms.abstractRift 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.en
dcterms.accessRightsOpen Access
dcterms.audienceAcademics
dcterms.audienceScientists
dcterms.available2025-01-25
dcterms.bibliographicCitationMulwa, 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.
dcterms.extentp. 1–11
dcterms.issued2025-01-25
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.publisherSvedbergOpen
dcterms.subjectmachine learning
dcterms.subjectmodelling
dcterms.subjectrift valley fever
dcterms.subjecttime series analysis
dcterms.subjectzoonoses
dcterms.typeJournal Article

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