Integrating global citizen science platforms to enable next-generation surveillance of invasive and vector mosquitoes

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,...

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Autores: 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.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/56689
Acceso en línea:http://hdl.handle.net/10230/56689
http://dx.doi.org/10.3390/insects13080675
Access Level:acceso abierto
Palabra clave:artificial intelligence
citizen science
computer vision
geographic information systems
invasive species
machine learning
mosquito monitoring
smartphone
vector-borne disease
vector surveillance
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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.artificial intelligencecitizen sciencecomputer visiongeographic information systemsinvasive speciesmachine learningmosquito monitoringsmartphonevector-borne diseasevector surveillanceMosquito-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.MDPI202320232022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/56689http://dx.doi.org/10.3390/insects13080675reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésInsects. 2022;13(8):675.http://mosquitodashboard.org/http://mosquitoalert.com/en/access-to-mosquito-alert-data-portalhttp://mosquito-alert.github.io/metadata_public_portal/meta_ipynb/tigapics.htmlhttp://globe.gov/globe-datahttps://github.com/IGES-Geospatialhttp://geospatial.strategies.org/http://inaturalist.org/info:eu-repo/grantAgreement/EC/H2020/874735info:eu-repo/grantAgreement/EC/H2020/853271© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/566892026-05-29T05:05:01Z
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.
artificial intelligence
citizen science
computer vision
geographic information systems
invasive species
machine learning
mosquito monitoring
smartphone
vector-borne disease
vector surveillance
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.subject.none.fl_str_mv artificial intelligence
citizen science
computer vision
geographic information systems
invasive species
machine learning
mosquito monitoring
smartphone
vector-borne disease
vector surveillance
topic artificial intelligence
citizen science
computer vision
geographic information systems
invasive species
machine learning
mosquito monitoring
smartphone
vector-borne disease
vector surveillance
description 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.
publishDate 2022
dc.date.none.fl_str_mv 2022
2023
2023
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 http://hdl.handle.net/10230/56689
http://dx.doi.org/10.3390/insects13080675
url http://hdl.handle.net/10230/56689
http://dx.doi.org/10.3390/insects13080675
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Insects. 2022;13(8):675.
http://mosquitodashboard.org/
http://mosquitoalert.com/en/access-to-mosquito-alert-data-portal
http://mosquito-alert.github.io/metadata_public_portal/meta_ipynb/tigapics.html
http://globe.gov/globe-data
https://github.com/IGES-Geospatial
http://geospatial.strategies.org/
http://inaturalist.org/
info:eu-repo/grantAgreement/EC/H2020/874735
info:eu-repo/grantAgreement/EC/H2020/853271
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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