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

2024-03-01Journal article Open access
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dc.contributor.authorGonzález-Pérez, María I.
dc.contributor.authorFaulhaber, Bastian
dc.contributor.authorAranda, Carles
dc.contributor.authorWilliams, Mark
dc.contributor.authorVillalonga, Pancraç
dc.contributor.authorSilva, Manuel
dc.contributor.authorCosta Osório, Hugo
dc.contributor.authorEncarnaçao, Joao
dc.contributor.authorTalavera, Sandra
dc.contributor.authorBusquets, Núria
dc.date.accessioned2025-03-10T15:59:18Z
dc.date.available2025-03-10T15:59:18Z
dc.date.issued2024-03-01
dc.description.abstractBackground: 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.pt_PT
dc.description.sponsorshipThis research was supported by the project VECTRACK. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 853758. This research was also supported by the project IDAlert. This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No. 101057554.
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationParasit Vectors. 2024 Mar 1;17(1):97. doi: 10.1186/s13071-024-06177-w
dc.identifier.doi10.1186/s13071-024-06177-wpt_PT
dc.identifier.issn1756-3305
dc.identifier.pmid38424626
dc.identifier.urihttp://hdl.handle.net/10400.18/10427
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherBioMed Central
dc.relationEarth observation service for preventive control of insect disease vectors
dc.relationInfectious Disease decision-support tools and Alert systems to build climate Resilience to emerging health Threats
dc.relation.hasversionhttps://parasitesandvectors.biomedcentral.com/articles/10.1186/s13071-024-06177-w
dc.relation.publisherversionhttps://doi.org/10.1186/s13071-024-06177-wpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAedespt_PT
dc.subjectCulexpt_PT
dc.subjectAutomated classificationpt_PT
dc.subjectField studypt_PT
dc.subjectMachine learningpt_PT
dc.subjectMosquito surveillancept_PT
dc.subjectOptical sensorpt_PT
dc.titleField evaluation of an automated mosquito surveillance system which classifies Aedes and Culex mosquitoes by genus and sexpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleEarth observation service for preventive control of insect disease vectors
oaire.awardTitleInfectious Disease decision-support tools and Alert systems to build climate Resilience to emerging health Threats
oaire.awardURIhttp://hdl.handle.net/10400.18/10425
oaire.awardURIhttp://hdl.handle.net/10400.18/10426
oaire.citation.issue1pt_PT
oaire.citation.startPage97
oaire.citation.titleParasites and Vectors
oaire.citation.volume17pt_PT
oaire.fundingStreamInnovation action
oaire.fundingStreamHORIZON Research and Innovation Actions
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isProjectOfPublicationfbd16e10-5ef1-4f5c-98df-4c0743ad1dad
relation.isProjectOfPublicatione019931d-1a63-4dbf-8e9f-9e56df114dee
relation.isProjectOfPublication.latestForDiscoveryfbd16e10-5ef1-4f5c-98df-4c0743ad1dad

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