ArticleParasites & vectors2024
Field evaluation of an automated mosquito surveillance system which classifies Aedes and Culex mosquitoes by genus and sex.
Article in Parasites & vectors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 17 citations in OpenAlex.
- Development and effectiveness test of an internet-based real-time monitoring device for malaria parasite vector mosquitoes.PLoS neglected tropical diseases · 2026Article
- Field Efficacy of Seven Nighttime Ultra-Low-Volume Spraying Strategies AgainstChina CDC weekly · 2026Article
- Advances in mosquito-borne disease surveillance using machine learning.New microbes and new infections · 2026Article
- An autonomous intelligent mosquito sentinel for field-deployed surveillance.Communications engineering · 2026Article
- Unravelling the activity rhythms of urban vector mosquitoes with smart-trap technology.Scientific reports · 2026Article
- How technologies are driving digital innovation in mosquito surveillance systems: a global scoping review over the past decade.BMJ public health · 2026Article
- Advancing insect monitoring: analysis of mel-frequency cepstral coefficients from optical signals for body orientation estimation.Applied physics. B, Lasers and optics · 2026Article
- Viral emergence and pandemic preparedness in a One Health framework.Nature reviews. Microbiology · 2026Review
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- High-resolution characterisation of diel activity rhythms inCurrent research in parasitology & vector-borne diseases · 2026Article
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- Automated age grading of female Culex pipiens by an optical sensor system coupled to a mosquito trap.Parasites & vectors · 2024Article
- Enhancing mosquito classification through self-supervised learning.Scientific reports · 2024Article
- An easier life to come for mosquito researchers: field-testing across Italy supports VECTRACK system for automatic counting, identification and absolute density estimation of Aedes albopictus and Culex pipiens adults.Parasites & vectors · 2024Article
- Automated classification of mixed populations of Aedes aegypti and Culex quinquefasciatus mosquitoes under field conditions.Parasites & vectors · 2024Article
- Large-scale performance assessment of the BG-Counter 2 used with two different mosquito traps.Parasites & vectors · 2024Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundMosquito-borne diseases are a major concern for public and veterinary health authorities, highlighting the importance of effective vector surveillance and control programs. Traditional surveillance methods are labor-intensive and do not provide high temporal resolution, which may hinder a full assessment of the risk of mosquito-borne pathogen transmission. Emerging technologies for automated remote mosquito monitoring have the potential to address these limitations; however, few studies have tested the performance of such systems in the field.
methodsIn the present work, an optical sensor coupled to the entrance of a standard mosquito suction trap was used to record 14,067 mosquito flights of Aedes and Culex genera at four temperature regimes in the laboratory, and the resulting dataset was used to train a machine learning (ML) model. The trap, sensor, and ML model, which form the core of an automated mosquito surveillance system, were tested in the field for two classification purposes: to discriminate Aedes and Culex mosquitoes from other insects that enter the trap and to classify the target mosquitoes by genus and sex. The field performance of the system was assessed using balanced accuracy and regression metrics by comparing the classifications made by the system with those made by the manual inspection of the trap.
resultsThe field system discriminated the target mosquitoes (Aedes and Culex genera) with a balanced accuracy of 95.5% and classified the genus and sex of those mosquitoes with a balanced accuracy of 88.8%. An analysis of the daily and seasonal temporal dynamics of Aedes and Culex mosquito populations was also performed using the time-stamped classifications from the system.
conclusionsThis study reports results for automated mosquito genus and sex classification using an optical sensor coupled to a mosquito trap in the field with highly balanced accuracy. The compatibility of the sensor with commercial mosquito traps enables the sensor to be integrated into conventional mosquito surveillance methods to provide accurate automatic monitoring with high temporal resolution of Aedes and Culex mosquitoes, two of the most concerning genera in terms of arbovirus transmission.
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