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Applied Surveillance Technologies for Mosquito Identification Across the Life Cycle: A Comparative Review

Muhammad Usman Raza, Wanchalerm Pora, Hitoshi Iba

Abstract


Reliable mosquito identification is essential for surveillance systems that support vector monitoring, risk assessment, and targeted intervention. However, conventional morphology-based identification remains labor-intensive, expertise-dependent, and difficult to scale for large monitoring programs. Advances in molecular diagnostics, imaging technologies, and artificial intelligence have enabled faster and more objective alternatives across the mosquito life cycle. This review presents a stage-aware comparative synthesis of mosquito identification methods for eggs, larvae, pupae, and adults, covering classical morphology, molecular and proteomic assays, geometric morphometrics, deep learning-based image analysis, acoustic sensing, and IoT-enabled smart traps. Structured as a narrative comparative review, the article evaluates these approaches in terms of diagnostic role, scalability, cost, infrastructure requirements, and field deployability. The analysis shows that each life stage supports a distinct surveillance function and that no single method is optimal across the full mosquito life cycle. Effective surveillance, therefore, requires tiered workflows that combine low-cost collection, automated screening, and selective confirmatory analysis according to operational needs.

Keywords



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DOI: 10.14416/j.asep.2026.07.012

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