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How technologies are driving digital innovation in mosquito surveillance systems: a global scoping review over the
Mattia Muraro1, Jiras Casimir Kpego Ligan1,2, Luca Bacco1
1Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy.
Objectives:
This review investigates the recent advancements aimed at optimising vector control through enhanced mosquito monitoring. Recent advancements in artificial intelligence (AI), internet of things (IoT) and remote sensing have been explored to enhance surveillance but a comprehensive review of their progress was needed.
Design:
Scoping review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines.
Data Sources:
Systematic searches were performed in PubMed and Scopus focusing on studies published between January 2014 and June 2024.
Eligibility Criteria:
Original studies focusing on mosquitoes and addressing the design, development, testing, implementation, as well as the evaluation of smart traps or modification, upgrade or digitisation of traditional mosquito traps or surveillance systems were included.
Data Extraction And Synthesis:
Key information relevant to this review was extracted into an Excel spreadsheet and narratively synthesised. We assessed the scientific quality of the studies included in this review based on their designs and especially on their resulting outcomes.
Results:
This work highlights the increasing integration of AI in mosquito identification and classification, complemented by environmental sensors for real-time monitoring of parameters such as temperature, humidity and landscape features. Additionally, the convergence of AI with IoT systems, robotic traps and remote sensing technologies, including drones and geographic information systems, enables the collection of granular data on mosquito populations and their habitats, facilitating proactive control measures. Overall, these smart surveillance technologies offer a transformative advancement in vector control, enabling more precise and efficient interventions. However, their widespread adoption will require thorough evaluation of cost-effectiveness, scalability and adaptability across diverse settings.
Conclusions:
This review maps the evolution of smart mosquito surveillance over the past decade, highlighting AI-driven species identification, IoT-enabled real-time monitoring and the integration of remote sensing technologies. It provides a structured analysis of how these innovations improve mosquito detection and data accuracy. These insights can guide further research, inform practical implementations and shape policy decisions in public health and mosquito surveillance.
