Application of Artificial Intelligence and Climate Data for Predicting Neglected Vector-Borne Diseases Beyond Malaria in Sub-Saharan Africa: A Scoping Review

Vector-borne diseases (VBDs) beyond malaria like dengue fever, Rift Valley fever, leishmaniasis, onchocerciasis, chikungunya, yellow fever, and African trypanosomiasis, represent a growing public health threat across sub-Saharan Africa (SSA), increasingly amplified by climate change. Despite this burden, the application of artificial intelligence (AI) and machine learning (ML) methods integrated with climate and environmental data for predicting these neglected VBDs in SSA remains poorly characterized in the literature. This scoping review aims to map the existing evidence on the use of AI and climate data for predicting neglected vector-borne diseases (excluding malaria) in sub-Saharan Africa. Following the JBI methodology for scoping reviews and reported in accordance with the PRISMA extension for Scoping Reviews (PRISMA-ScR), we will systematically search PubMed/MEDLINE, Embase, Scopus, Web of Science, IEEE Xplore, African Index Medicus, and Google Scholar for studies published from January 2000 to present. Two independent reviewers will screen all records and extract data using a standardized charting form. The review will characterize the AI/ML methods used, climate and environmental data sources employed, diseases and countries studied, model validation approaches, and the extent to which model outputs are linked to policy or vector control decision-making. Findings will be synthesized narratively and presented as an evidence gap map to guide future primary research and surveillance investment in SSA.