Field evaluation of an automated mosquito surveillance system which classifies Aedes and Culex mosquitoes by genus and sex

Abstract

Background Mosquito‑borne diseases are a major concern for public and veterinary health authorities, highlighting the importance of efective 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 feld. Methods In the present work, an optical sensor coupled to the entrance of a standard mosquito suction trap was used to record 14,067 mosquito fights 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 feld for two classifcation purposes: to discriminate Aedes and Culex mosquitoes from other insects that enter the trap and to classify the target mosqui‑ toes by genus and sex. The feld performance of the system was assessed using balanced accuracy and regression metrics by comparing the classifcations made by the system with those made by the manual inspection of the trap. Results The feld system discriminated the target mosquitoes (Aedes and Culex genera) with a balanced accuracy of 95.5% and classifed 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 classifcations from the system. Conclusions This study reports results for automated mosquito genus and sex classifcation using an optical sensor coupled to a mosquito trap in the feld with highly balanced accuracy. The compatibility of the sensor with commer‑ cial 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 con‑ cerning genera in terms of arbovirus transmission.