Integrating Global Citizen Science Platforms to Enable Next-Generation Surveillance of Invasive and Vector Mosquitoes

Este artículo contiene 24 páginas, 6 figuras.

Bibliographic Details
Authors: Carney, Ryan M., Mapes, Connor, Low, Russanne D., Long, Alex, Bowser, Anne, Durieux, David, Rivera, Karlene, Dekramanjian, Berj, Bartumeus, Frederic, Guerrero, Daniel, Seltzer, Carrie E., Azam, Farhat, Chellappan, Sriram, Palmer, John R. B.
Format: article
Status:Published version
Publication Date:2022
Country:España
Institution:Consejo Superior de Investigaciones Científicas (CSIC)
Repository:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/279184
Online Access:http://hdl.handle.net/10261/279184
Access Level:Open access
Keyword:vector-borne disease
vector surveillance
Artificial intelligence
Citizen science
computer vision
Geographic information systems
invasive species
Machine learning
mosquito monitoring
smartphone
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spelling Integrating Global Citizen Science Platforms to Enable Next-Generation Surveillance of Invasive and Vector MosquitoesCarney, Ryan M.Mapes, ConnorLow, Russanne D.Long, AlexBowser, AnneDurieux, DavidRivera, KarleneDekramanjian, BerjBartumeus, FredericGuerrero, DanielSeltzer, Carrie E.Azam, FarhatChellappan, SriramPalmer, John R. B.vector-borne diseasevector surveillanceArtificial intelligenceCitizen sciencecomputer visionGeographic information systemsinvasive speciesMachine learningmosquito monitoringsmartphoneEste artículo contiene 24 páginas, 6 figuras.Mosquito-borne diseases continue to ravage humankind with >700 million infections and nearly one million deaths every year. Yet only a small percentage of the >3500 mosquito species transmit diseases, necessitating both extensive surveillance and precise identification. Unfortunately, such efforts are costly, time-consuming, and require entomological expertise. As envisioned by the Global Mosquito Alert Consortium, citizen science can provide a scalable solution. However, disparate data standards across existing platforms have thus far precluded truly global integration. Here, utilizing Open Geospatial Consortium standards, we harmonized four data streams from three established mobile apps—Mosquito Alert, iNaturalist, and GLOBE Observer’s Mosquito Habitat Mapper and Land Cover—to facilitate interoperability and utility for researchers, mosquito control personnel, and policymakers. We also launched coordinated media campaigns that generated unprecedented numbers and types of observations, including successfully capturing the first images of targeted invasive and vector species. Additionally, we leveraged pooled image data to develop a toolset of artificial intelligence algorithms for future deployment in taxonomic and anatomical identification. Ultimately, by harnessing the combined powers of citizen science and artificial intelligence, we establish a next-generation surveillance framework to serve as a united front to combat the ongoing threat of mosquito-borne diseases worldwide.This research was funded by the National Science Foundation under Grant No. IIS-2014547 to R.M.C., S.C., R.D.L. and A.B. The GLOBE Observer app and citizen science programming are supported through National Aeronautics and Space Administration (NASA) cooperative agreement NNX16AE28A to the Institute for Global Environmental Strategies (IGES) for the NASA Earth Science Education Collaborative (NESEC, PI: Theresa Schwerin). F.B. and J.R.B.P. acknowledge funding from: (a) the European Commission, under Grants CA17108 (AIM-COST Action), 874735 (VEO), 853271 (H-MIP), and 2020/2094 (NextGenerationEU, through CSIC’s Global Health Platform, PTI Salud Global); (b) the Dutch National Research Agenda (NWA), under Grant NWA/00686468; and (c) “la Caixa” Foundation, under Grant HR19-00336.Peer reviewedMultidisciplinary Digital Publishing InstituteConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202220222022info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/279184reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttps://doi.org/10.3390/insects13080675Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2791842026-05-22T06:33:51Z
dc.title.none.fl_str_mv Integrating Global Citizen Science Platforms to Enable Next-Generation Surveillance of Invasive and Vector Mosquitoes
title Integrating Global Citizen Science Platforms to Enable Next-Generation Surveillance of Invasive and Vector Mosquitoes
spellingShingle Integrating Global Citizen Science Platforms to Enable Next-Generation Surveillance of Invasive and Vector Mosquitoes
Carney, Ryan M.
vector-borne disease
vector surveillance
Artificial intelligence
Citizen science
computer vision
Geographic information systems
invasive species
Machine learning
mosquito monitoring
smartphone
title_short Integrating Global Citizen Science Platforms to Enable Next-Generation Surveillance of Invasive and Vector Mosquitoes
title_full Integrating Global Citizen Science Platforms to Enable Next-Generation Surveillance of Invasive and Vector Mosquitoes
title_fullStr Integrating Global Citizen Science Platforms to Enable Next-Generation Surveillance of Invasive and Vector Mosquitoes
title_full_unstemmed Integrating Global Citizen Science Platforms to Enable Next-Generation Surveillance of Invasive and Vector Mosquitoes
title_sort Integrating Global Citizen Science Platforms to Enable Next-Generation Surveillance of Invasive and Vector Mosquitoes
dc.creator.none.fl_str_mv Carney, Ryan M.
Mapes, Connor
Low, Russanne D.
Long, Alex
Bowser, Anne
Durieux, David
Rivera, Karlene
Dekramanjian, Berj
Bartumeus, Frederic
Guerrero, Daniel
Seltzer, Carrie E.
Azam, Farhat
Chellappan, Sriram
Palmer, John R. B.
author Carney, Ryan M.
author_facet Carney, Ryan M.
Mapes, Connor
Low, Russanne D.
Long, Alex
Bowser, Anne
Durieux, David
Rivera, Karlene
Dekramanjian, Berj
Bartumeus, Frederic
Guerrero, Daniel
Seltzer, Carrie E.
Azam, Farhat
Chellappan, Sriram
Palmer, John R. B.
author_role author
author2 Mapes, Connor
Low, Russanne D.
Long, Alex
Bowser, Anne
Durieux, David
Rivera, Karlene
Dekramanjian, Berj
Bartumeus, Frederic
Guerrero, Daniel
Seltzer, Carrie E.
Azam, Farhat
Chellappan, Sriram
Palmer, John R. B.
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv vector-borne disease
vector surveillance
Artificial intelligence
Citizen science
computer vision
Geographic information systems
invasive species
Machine learning
mosquito monitoring
smartphone
topic vector-borne disease
vector surveillance
Artificial intelligence
Citizen science
computer vision
Geographic information systems
invasive species
Machine learning
mosquito monitoring
smartphone
description Este artículo contiene 24 páginas, 6 figuras.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/279184
url http://hdl.handle.net/10261/279184
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.3390/insects13080675
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869425363946307584
score 15,812455

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