Automated age grading of female Culex pipiens by an optical sensor system coupled to a mosquito trap
The age distribution of a mosquito population is a major determinant of its vectorial capacity. To contribute to disease transmission, a competent mosquito vector, carrying a pathogen, must live longer than the extrinsic incubation period of that pathogen to enable transmission to a new host. As suc...
| Autores: | , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:320240 |
| Acceso en línea: | https://ddd.uab.cat/record/320240 https://dx.doi.org/urn:doi:10.1186/s13071-024-06606-w |
| Access Level: | acceso abierto |
| Palabra clave: | Culex pipiens Mosquito vectors Age grading Chronological age Optical sensor Machine learning |
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Automated age grading of female Culex pipiens by an optical sensor system coupled to a mosquito trapGonzález-Pérez, María I.Faulhaber, BastianWilliams, MarkEncarnaçao, JoaoVillalonga, PancraçAranda Pallero, Carles|||0000-0003-1496-8306Busquets, Núria|||0000-0001-5246-8260Culex pipiensMosquito vectorsAge gradingChronological ageOptical sensorMachine learningThe age distribution of a mosquito population is a major determinant of its vectorial capacity. To contribute to disease transmission, a competent mosquito vector, carrying a pathogen, must live longer than the extrinsic incubation period of that pathogen to enable transmission to a new host. As such, determining the age of female mosquitoes is of significant interest for vector-borne diseases surveillance and control programs. In this contribution, an automated age-grading system was developed to classify the age of female Culex pipiens, which is the primary vector of West Nile virus and other pathogens of medical and veterinary importance in northern latitudes. The system comprises an optical wingbeat sensor coupled to the entrance of a mosquito trap and a machine learning model. Three age classes were used in the study: young (2-4 days), middle (7-9 days) and old (14-16 days). From a balanced dataset of flight data, four features were extracted: wingbeat fundamental frequency, spectrogram, power spectral density and Mel frequency cepstral coefficients. The features were used for training with the XGBoost algorithm to generate a model for age classification. The best performing model was trained with the power spectral density feature on two age classes, ≤ 4 days old and ≥ 7 days old, and had an accuracy of 71.8%. An automated mosquito age-grading system was applied for the first time to our knowledge for automated age classification in mosquitoes; and complements the mosquito genus and sex classification capability of the system reported in our previous work. The system may find use in mosquito-borne disease surveillance and control to help to discriminate young mosquitoes (≤ 4 days old) from older mosquitoes, which may act as vectors of arboviruses. 22024-01-0120242024-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/320240https://dx.doi.org/urn:doi:10.1186/s13071-024-06606-wreponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengEuropean Commission https://doi.org/10.13039/501100000780 853758European Commission https://doi.org/10.13039/501100000780 101099283open accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:3202402026-06-06T12:50:31Z |
| dc.title.none.fl_str_mv |
Automated age grading of female Culex pipiens by an optical sensor system coupled to a mosquito trap |
| title |
Automated age grading of female Culex pipiens by an optical sensor system coupled to a mosquito trap |
| spellingShingle |
Automated age grading of female Culex pipiens by an optical sensor system coupled to a mosquito trap González-Pérez, María I. Culex pipiens Mosquito vectors Age grading Chronological age Optical sensor Machine learning |
| title_short |
Automated age grading of female Culex pipiens by an optical sensor system coupled to a mosquito trap |
| title_full |
Automated age grading of female Culex pipiens by an optical sensor system coupled to a mosquito trap |
| title_fullStr |
Automated age grading of female Culex pipiens by an optical sensor system coupled to a mosquito trap |
| title_full_unstemmed |
Automated age grading of female Culex pipiens by an optical sensor system coupled to a mosquito trap |
| title_sort |
Automated age grading of female Culex pipiens by an optical sensor system coupled to a mosquito trap |
| dc.creator.none.fl_str_mv |
González-Pérez, María I. Faulhaber, Bastian Williams, Mark Encarnaçao, Joao Villalonga, Pancraç Aranda Pallero, Carles|||0000-0003-1496-8306 Busquets, Núria|||0000-0001-5246-8260 |
| author |
González-Pérez, María I. |
| author_facet |
González-Pérez, María I. Faulhaber, Bastian Williams, Mark Encarnaçao, Joao Villalonga, Pancraç Aranda Pallero, Carles|||0000-0003-1496-8306 Busquets, Núria|||0000-0001-5246-8260 |
| author_role |
author |
| author2 |
Faulhaber, Bastian Williams, Mark Encarnaçao, Joao Villalonga, Pancraç Aranda Pallero, Carles|||0000-0003-1496-8306 Busquets, Núria|||0000-0001-5246-8260 |
| author2_role |
author author author author author author |
| dc.subject.none.fl_str_mv |
Culex pipiens Mosquito vectors Age grading Chronological age Optical sensor Machine learning |
| topic |
Culex pipiens Mosquito vectors Age grading Chronological age Optical sensor Machine learning |
| description |
The age distribution of a mosquito population is a major determinant of its vectorial capacity. To contribute to disease transmission, a competent mosquito vector, carrying a pathogen, must live longer than the extrinsic incubation period of that pathogen to enable transmission to a new host. As such, determining the age of female mosquitoes is of significant interest for vector-borne diseases surveillance and control programs. In this contribution, an automated age-grading system was developed to classify the age of female Culex pipiens, which is the primary vector of West Nile virus and other pathogens of medical and veterinary importance in northern latitudes. The system comprises an optical wingbeat sensor coupled to the entrance of a mosquito trap and a machine learning model. Three age classes were used in the study: young (2-4 days), middle (7-9 days) and old (14-16 days). From a balanced dataset of flight data, four features were extracted: wingbeat fundamental frequency, spectrogram, power spectral density and Mel frequency cepstral coefficients. The features were used for training with the XGBoost algorithm to generate a model for age classification. The best performing model was trained with the power spectral density feature on two age classes, ≤ 4 days old and ≥ 7 days old, and had an accuracy of 71.8%. An automated mosquito age-grading system was applied for the first time to our knowledge for automated age classification in mosquitoes; and complements the mosquito genus and sex classification capability of the system reported in our previous work. The system may find use in mosquito-borne disease surveillance and control to help to discriminate young mosquitoes (≤ 4 days old) from older mosquitoes, which may act as vectors of arboviruses. |
| publishDate |
2024 |
| dc.date.none.fl_str_mv |
2 2024-01-01 2024 2024-01-01 |
| dc.type.none.fl_str_mv |
Article http://purl.org/coar/resource_type/c_6501 VoR http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| dc.type.openaire.fl_str_mv |
info:eu-repo/semantics/article |
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article |
| dc.identifier.none.fl_str_mv |
https://ddd.uab.cat/record/320240 https://dx.doi.org/urn:doi:10.1186/s13071-024-06606-w |
| url |
https://ddd.uab.cat/record/320240 https://dx.doi.org/urn:doi:10.1186/s13071-024-06606-w |
| dc.language.none.fl_str_mv |
Inglés eng |
| language_invalid_str_mv |
Inglés |
| language |
eng |
| dc.relation.none.fl_str_mv |
European Commission https://doi.org/10.13039/501100000780 853758 European Commission https://doi.org/10.13039/501100000780 101099283 |
| dc.rights.none.fl_str_mv |
open access http://purl.org/coar/access_right/c_abf2 https://creativecommons.org/licenses/by/4.0/ |
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info:eu-repo/semantics/openAccess |
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open access http://purl.org/coar/access_right/c_abf2 https://creativecommons.org/licenses/by/4.0/ |
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openAccess |
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application/pdf |
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reponame:Dipòsit Digital de Documents de la UAB instname:Universitat Autònoma de Barcelona |
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Universitat Autònoma de Barcelona |
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Dipòsit Digital de Documents de la UAB |
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