The role of artificial intelligence for dengue prevention, control, and management: A technical narrative review

Dengue fever remains a significant global health threat, particularly in tropical and subtropical regions, where rapid urbanization and climate variability exacerbate its spread. Traditional surveillance and control systems often struggle with delayed reporting, limited predictive capacity, and resource constraints. Artificial Intelligence (AI) presents a transformative opportunity to enhance dengue prevention, control, and management through data-driven decision-making. This review synthesizes current applications of AI across key domains: outbreak forecasting, vector control, clinical management, and public health intervention. Machine learning models have demonstrated promising accuracy in predicting dengue outbreaks using environmental, climatic, and epidemiological data. AI-powered image recognition and autonomous systems support more effective mosquito monitoring and habitat targeting. In clinical settings, AI assists with early diagnosis, severity prediction, and treatment planning. Additionally, AI-driven tools facilitate real-time analysis of behavioral data and community engagement through mobile platforms. However, challenges remain, including data standardization, model interpretability, and ethical concerns around data privacy. This review highlights the need for interdisciplinary collaboration and robust infrastructure to scale and sustain AI-based systems. With continued innovation and policy support, AI has the potential to significantly reduce dengue burden and build more resilient public health systems.