Mosquito-borne diseases remain a major global health challenge, disproportionately impacting low- and middle-income countries. Despite traditional control and surveillance efforts, many of these diseases are resurging, driven by climate change, urbanisation, and global trade and travel. In recent years, machine learning, a subset of artificial intelligence, has emerged as a powerful tool for supporting the surveillance of MBDs. This systematic review, following PRISMA guidelines, examines 81 studies published between 2010 and 2024 to provide an overview of the current state of the art in applying machine learning techniques in the surveillance of malaria, dengue, Zika, chikungunya, yellow fever, and other mosquito-borne diseases. We highlight current trends in the use of machine learning techniques for forecasting, risk mapping, real-time disease monitoring, and vector/host ecology, and identify the most frequently used machine learning algorithms, including support vector machines, random forests, decision trees, and logistic regression. While machine learning models have shown promising predictive performance in some studies, their effectiveness depends on the availability, quality, and contextual relevance of the data. Gaps remain in model validation, implementation in low-resource settings, and inclusion of animal health data. Our systematic review outlines key findings, identifies research gaps, and proposes strategies for integrating machine learning in future mosquito-borne disease control efforts. • Machine learning has several uses in mosquito-borne disease surveillance. • Random forest and support vector machines are the most used techniques. • Interdisciplinary data integration is essential for effective surveillance.
Geffroy et al. (Wed,) studied this question.