Assessing and correcting neighborhood socioeconomic spatial sampling biases in citizen science mosquito data collection

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• dc.contributor.author Padilla-Pozo, Álvaro
• dc.contributor.author Bartumeus, Frederic
• dc.contributor.author Montalvo, Tomás
• dc.contributor.author Sanpera-Calbet, Isis
• dc.contributor.author Valsecchi, Andrea
• dc.contributor.author Palmer, John R. B.
• dc.date.accessioned 2025-03-14T10:05:46Z
• dc.date.available 2025-03-14T10:05:46Z
• dc.date.issued 2024
• dc.description Includes supplementary materials for the online appendix.
• dc.description.abstract Climatic, ecological, and socioeconomic factors are facilitating the spread of mosquito-borne diseases, heightening the importance of vector surveillance and control. Citizen science is proving to be an effective tool to track mosquito populations, but methods are needed to detect and account for small scale sampling biases in citizen science surveillance. In this article we combine two types of traditional mosquito surveillance records with data from the Mosquito Alert citizen science system to explore the ways in which the socioeconomic characteristics of urban neighborhoods result in sampling biases in citizen scientists’ mosquito reports, while also shaping the spatial distribution of mosquito populations themselves. We use Barcelona, Spain, as an example, and focus on Aedes albopictus, an invasive vector species of concern worldwide. Our results suggest citizen scientists’ sampling effort is focused more in Barcelona’s lower and middle income census tracts than in its higher income ones, whereas Ae. albopictus populations are concentrated in the city’s upper-middle income tracts. High resolution estimates of the spatial distribution of Ae. albopictus risk can be improved by controlling for citizen scientists’ sampling effort, making it possible to provide better insights for efficiently targeting control efforts. Our methodology can be replicated in other cities faced with vector mosquitoes to improve public health responses to mosquito-borne diseases, which impose massive burdens on communities worldwide.
• dc.description.sponsorship This research received funding from the ”la Caixa” Foundation, under Grant HR19-00336, and from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant agreement No. 853271).
• dc.format.mimetype application/pdf
• dc.identifier.citation Padilla-Pozo Á, Bartumeus F, Montalvo T, Sanpera-Calbet I, Valsecchi A, Palmer JRB. Assessing and correcting neighborhood socioeconomic spatial sampling biases in citizen science mosquito data collection. Sci Rep. 2024;14:22462. DOI: 10.1038/s41598-024-73416-6
• dc.identifier.doi http://dx.doi.org/10.1038/s41598-024-73416-6
• dc.identifier.issn 2045-2322
• dc.identifier.uri http://hdl.handle.net/10230/69940
• dc.language.iso eng
• dc.publisher Springer
• dc.relation.ispartof Scientific Reports. 2024;14:22462
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/853271
• dc.rights © The Author(s) 2024. 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.keyword Mosquito-borne diseases
• dc.subject.keyword Vector control
• dc.subject.keyword Vector surveillance
• dc.subject.keyword Aedes albopictus
• dc.subject.keyword Citizen science
• dc.subject.keyword Social inequality
• dc.title Assessing and correcting neighborhood socioeconomic spatial sampling biases in citizen science mosquito data collection
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion