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 effective vector surveillance and control programs. Traditional surveillance methods are labor-intensive and do not provide high temporal resolution, which may...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-03-01
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| Series: | Parasites & Vectors |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13071-024-06177-w |
| _version_ | 1797275690497736704 |
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| author | María I. González-Pérez Bastian Faulhaber Carles Aranda Mark Williams Pancraç Villalonga Manuel Silva Hugo Costa Osório Joao Encarnaçao Sandra Talavera Núria Busquets |
| author_facet | María I. González-Pérez Bastian Faulhaber Carles Aranda Mark Williams Pancraç Villalonga Manuel Silva Hugo Costa Osório Joao Encarnaçao Sandra Talavera Núria Busquets |
| author_sort | María I. González-Pérez |
| collection | DOAJ |
| description | Abstract Background Mosquito-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. 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 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. Results The 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. Conclusions This 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. Graphical Abstract |
| first_indexed | 2024-03-07T15:17:07Z |
| format | Article |
| id | doaj.art-820790631b7e4fc5a120cb75d6daf324 |
| institution | Directory Open Access Journal |
| issn | 1756-3305 |
| language | English |
| last_indexed | 2024-03-07T15:17:07Z |
| publishDate | 2024-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Parasites & Vectors |
| spelling | doaj.art-820790631b7e4fc5a120cb75d6daf3242024-03-05T17:51:02ZengBMCParasites & Vectors1756-33052024-03-0117111310.1186/s13071-024-06177-wField evaluation of an automated mosquito surveillance system which classifies Aedes and Culex mosquitoes by genus and sexMaría I. González-Pérez0Bastian Faulhaber1Carles Aranda2Mark Williams3Pancraç Villalonga4Manuel Silva5Hugo Costa Osório6Joao Encarnaçao7Sandra Talavera8Núria Busquets9IRTA, Programa de Sanitat Animal, Centre de Recerca en Sanitat Animal (CReSA), Campus de la Universitat Autònoma de Barcelona (UAB)Irideon S.LIRTA, Programa de Sanitat Animal, Centre de Recerca en Sanitat Animal (CReSA), Campus de la Universitat Autònoma de Barcelona (UAB)Irideon S.LIrideon S.LNational Institute of Health/Centre for Vectors and Infectious Diseases ResearchNational Institute of Health/Centre for Vectors and Infectious Diseases ResearchIrideon S.LIRTA, Programa de Sanitat Animal, Centre de Recerca en Sanitat Animal (CReSA), Campus de la Universitat Autònoma de Barcelona (UAB)IRTA, Programa de Sanitat Animal, Centre de Recerca en Sanitat Animal (CReSA), Campus de la Universitat Autònoma de Barcelona (UAB)Abstract Background Mosquito-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. 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 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. Results The 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. Conclusions This 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. Graphical Abstracthttps://doi.org/10.1186/s13071-024-06177-wAedesAutomated classificationCulexField studyMachine learningMosquito surveillance |
| spellingShingle | María I. González-Pérez Bastian Faulhaber Carles Aranda Mark Williams Pancraç Villalonga Manuel Silva Hugo Costa Osório Joao Encarnaçao Sandra Talavera Núria Busquets Field evaluation of an automated mosquito surveillance system which classifies Aedes and Culex mosquitoes by genus and sex Parasites & Vectors Aedes Automated classification Culex Field study Machine learning Mosquito surveillance |
| title | Field evaluation of an automated mosquito surveillance system which classifies Aedes and Culex mosquitoes by genus and sex |
| title_full | Field evaluation of an automated mosquito surveillance system which classifies Aedes and Culex mosquitoes by genus and sex |
| title_fullStr | Field evaluation of an automated mosquito surveillance system which classifies Aedes and Culex mosquitoes by genus and sex |
| title_full_unstemmed | Field evaluation of an automated mosquito surveillance system which classifies Aedes and Culex mosquitoes by genus and sex |
| title_short | Field evaluation of an automated mosquito surveillance system which classifies Aedes and Culex mosquitoes by genus and sex |
| title_sort | field evaluation of an automated mosquito surveillance system which classifies aedes and culex mosquitoes by genus and sex |
| topic | Aedes Automated classification Culex Field study Machine learning Mosquito surveillance |
| url | https://doi.org/10.1186/s13071-024-06177-w |
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