ArticleBMJ public health2026
How technologies are driving digital innovation in mosquito surveillance systems: a global scoping review over the past decade.
Article in BMJ public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: This review investigates the recent advancements aimed at optimising vector control through enhanced mosquito monitoring. Recent advancements in 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 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.