Augmenting community-driven vector surveillance with automated image classification: lessons from the Artificial Intelligence Mosquito Alert (AIMA) system

The Mosquito Alert (MA) platform leverages artificial intelligence to enhance community-driven mosquito surveillance by automatically identifying mosquito species from geolocated images submitted via a mobile app. This empowers the public to report both native and invasive mosquitoes of public healt...

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Detalles Bibliográficos
Autores: Falk, Monika, Garriga, Joan, Eritja, Roger, Sanpera-Calbet, Isis, Pou, Enric, Richter-Boix, Alex, Palmer, John R. B., Bartumeus, Frederic
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/72665
Acceso en línea:https://hdl.handle.net/10230/72665
http://dx.doi.org/10.1016/j.epidem.2025.100863
Access Level:acceso abierto
Palabra clave:Artificial intelligence
Citizen science
Early warning systems
Vector risk visualization
Mosquito-borne diseases
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spelling Augmenting community-driven vector surveillance with automated image classification: lessons from the Artificial Intelligence Mosquito Alert (AIMA) systemFalk, MonikaGarriga, JoanEritja, RogerSanpera-Calbet, IsisPou, EnricRichter-Boix, AlexPalmer, John R. B.Bartumeus, FredericArtificial intelligenceCitizen scienceEarly warning systemsVector risk visualizationMosquito-borne diseasesThe Mosquito Alert (MA) platform leverages artificial intelligence to enhance community-driven mosquito surveillance by automatically identifying mosquito species from geolocated images submitted via a mobile app. This empowers the public to report both native and invasive mosquitoes of public health relevance, contributing to early detection and monitoring efforts. The Artificial Intelligence Mosquito Alert (AIMA) system integrates machine learning image classification within an automated backend pipeline to enable real-time triaging of submissions: critical reports are flagged for expert review, routine cases are classified automatically, and contributors receive immediate feedback fostering participant engagement. By automating routine identifications, the system reduces the burden on experts, allowing them to focus on complex or ambiguous cases that require taxonomic expertise. This study focuses on two AIMA operational periods in 2023 and 2024. We evaluate model updates and performance across these years, highlighting both progress achieved and remaining limitations under real-world citizen science conditions. The most reliably classified species across both models were Aedes albopictus and Culex sp., whereas Aedes aegypti remained difficult to identify. Despite its limitations, AIMA remains central to enabling scalable, responsive, and intelligent mosquito vector surveillance, substantially reducing the time experts must devote to routine identifications. Functioning as an Early Warning System (EWS), MA produces real-time distribution maps of invasive species and rapidly delivers actionable information to public health authorities, facilitating timely responses and intervention.Work supported by funding from the European Union's Horizon Europe program: E4Warning (101086640); VEO (874735); AIM COST (CA17108).Elsevier2026202620252026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/10230/72665http://dx.doi.org/10.1016/j.epidem.2025.100863reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésEpidemics. 2025 Dec;53:100863info:eu-repo/grantAgreement/EC/HE/101086640info:eu-repo/grantAgreement/EC/H2020/874735© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).http://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/726652026-06-12T07:21:37Z
dc.title.none.fl_str_mv Augmenting community-driven vector surveillance with automated image classification: lessons from the Artificial Intelligence Mosquito Alert (AIMA) system
title Augmenting community-driven vector surveillance with automated image classification: lessons from the Artificial Intelligence Mosquito Alert (AIMA) system
spellingShingle Augmenting community-driven vector surveillance with automated image classification: lessons from the Artificial Intelligence Mosquito Alert (AIMA) system
Falk, Monika
Artificial intelligence
Citizen science
Early warning systems
Vector risk visualization
Mosquito-borne diseases
title_short Augmenting community-driven vector surveillance with automated image classification: lessons from the Artificial Intelligence Mosquito Alert (AIMA) system
title_full Augmenting community-driven vector surveillance with automated image classification: lessons from the Artificial Intelligence Mosquito Alert (AIMA) system
title_fullStr Augmenting community-driven vector surveillance with automated image classification: lessons from the Artificial Intelligence Mosquito Alert (AIMA) system
title_full_unstemmed Augmenting community-driven vector surveillance with automated image classification: lessons from the Artificial Intelligence Mosquito Alert (AIMA) system
title_sort Augmenting community-driven vector surveillance with automated image classification: lessons from the Artificial Intelligence Mosquito Alert (AIMA) system
dc.creator.none.fl_str_mv Falk, Monika
Garriga, Joan
Eritja, Roger
Sanpera-Calbet, Isis
Pou, Enric
Richter-Boix, Alex
Palmer, John R. B.
Bartumeus, Frederic
author Falk, Monika
author_facet Falk, Monika
Garriga, Joan
Eritja, Roger
Sanpera-Calbet, Isis
Pou, Enric
Richter-Boix, Alex
Palmer, John R. B.
Bartumeus, Frederic
author_role author
author2 Garriga, Joan
Eritja, Roger
Sanpera-Calbet, Isis
Pou, Enric
Richter-Boix, Alex
Palmer, John R. B.
Bartumeus, Frederic
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Artificial intelligence
Citizen science
Early warning systems
Vector risk visualization
Mosquito-borne diseases
topic Artificial intelligence
Citizen science
Early warning systems
Vector risk visualization
Mosquito-borne diseases
description The Mosquito Alert (MA) platform leverages artificial intelligence to enhance community-driven mosquito surveillance by automatically identifying mosquito species from geolocated images submitted via a mobile app. This empowers the public to report both native and invasive mosquitoes of public health relevance, contributing to early detection and monitoring efforts. The Artificial Intelligence Mosquito Alert (AIMA) system integrates machine learning image classification within an automated backend pipeline to enable real-time triaging of submissions: critical reports are flagged for expert review, routine cases are classified automatically, and contributors receive immediate feedback fostering participant engagement. By automating routine identifications, the system reduces the burden on experts, allowing them to focus on complex or ambiguous cases that require taxonomic expertise. This study focuses on two AIMA operational periods in 2023 and 2024. We evaluate model updates and performance across these years, highlighting both progress achieved and remaining limitations under real-world citizen science conditions. The most reliably classified species across both models were Aedes albopictus and Culex sp., whereas Aedes aegypti remained difficult to identify. Despite its limitations, AIMA remains central to enabling scalable, responsive, and intelligent mosquito vector surveillance, substantially reducing the time experts must devote to routine identifications. Functioning as an Early Warning System (EWS), MA produces real-time distribution maps of invasive species and rapidly delivers actionable information to public health authorities, facilitating timely responses and intervention.
publishDate 2025
dc.date.none.fl_str_mv 2025
2026
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/10230/72665
http://dx.doi.org/10.1016/j.epidem.2025.100863
url https://hdl.handle.net/10230/72665
http://dx.doi.org/10.1016/j.epidem.2025.100863
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Epidemics. 2025 Dec;53:100863
info:eu-repo/grantAgreement/EC/HE/101086640
info:eu-repo/grantAgreement/EC/H2020/874735
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
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