Deep learning identification for citizen science surveillance of tiger mosquitoes

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• dc.contributor.author Pataki, Balint Armin
• dc.contributor.author Garriga, Joan
• dc.contributor.author Eritja, Roger
• dc.contributor.author Palmer, John R. B.
• dc.contributor.author Bartumeus, Frederic
• dc.contributor.author Csabai, Istvan
• dc.date.accessioned 2022-11-15T07:06:16Z
• dc.date.available 2022-11-15T07:06:16Z
• dc.date.issued 2021
• dc.description.abstract Global monitoring of disease vectors is undoubtedly becoming an urgent need as the human population rises and becomes increasingly mobile, international commercial exchanges increase, and climate change expands the habitats of many vector species. Traditional surveillance of mosquitoes, vectors of many diseases, relies on catches, which requires regular manual inspection and reporting, and dedicated personnel, making large-scale monitoring difficult and expensive. New approaches are solving the problem of scalability by relying on smartphones and the Internet to enable novel community-based and digital observatories, where people can upload pictures of mosquitoes whenever they encounter them. An example is the Mosquito Alert citizen science system, which includes a dedicated mobile phone app through which geotagged images are collected. This system provides a viable option for monitoring the spread of various mosquito species across the globe, although it is partly limited by the quality of the citizen scientists’ photos. To make the system useful for public health agencies, and to give feedback to the volunteering citizens, the submitted images are inspected and labeled by entomology experts. Although citizen-based data collection can greatly broaden disease-vector monitoring scales, manual inspection of each image is not an easily scalable option in the long run, and the system could be improved through automation. Based on Mosquito Alert’s curated database of expert-validated mosquito photos, we trained a deep learning model to find tiger mosquitoes (Aedes albopictus), a species that is responsible for spreading chikungunya, dengue, and Zika among other diseases. The highly accurate 0.96 area under the receiver operating characteristic curve score promises not only a helpful pre-selector for the expert validation process but also an automated classifier giving quick feedback to the app participants, which may help to keep them motivated. In the paper, we also explored the possibilities of using the model to improve future data collection quality as a feedback loop.
• dc.description.sponsorship This work was in part financed by EU Horizon 2020 program Grant agreement “VEO” No. 874735. The research was supported by the Ministry of Innovation and Technology NRDI Office within the framework of the Artificial Intelligence National Laboratory Program. We also acknowledge the Mosquito team (http://www.mosquitoalert.com/en/about-us/team/) for their work in keeping the system operative, even in harsh financial times, and most especially the team of volunteer entomology experts that have validated mosquito pictures from Mosquito Alert during the period 2014–2019: Mikel Bengoa, Sarah Delacour, Ignacio Ruiz, Maria Àngeles Puig, Pedro María Alarcon-Elbal, Rosario Melero-Alcíbar, Simone Mariani, and Santi Escartin. Finally, we would like to thank the Mosquito Alert community (anonymous citizens) who have participated year by year, making all this data collection system worth it.
• dc.format.mimetype application/pdf
• dc.identifier.citation Pataki BA, Garriga J, Eritja R, Palmer JRB, Bartumeus F, Csabai I. Deep learning identification for citizen science surveillance of tiger mosquitoes. Sci Rep. 2021 Feb 25;11:4718. DOI: 10.1038/s41598-021-83657-4
• dc.identifier.doi http://dx.doi.org/10.1038/s41598-021-83657-4
• dc.identifier.issn 2045-2322
• dc.identifier.uri http://hdl.handle.net/10230/54838
• dc.language.iso eng
• dc.publisher Nature Research
• dc.relation.ispartof Scientific Reports. 2021 Feb 25;11:4718
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/874735
• dc.rights This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.other Mosquit tigre
• dc.subject.other Malalties transmissibles -- Transmissió
• dc.subject.other Canvis climàtics
• dc.title Deep learning identification for citizen science surveillance of tiger mosquitoes
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion