Modelling the spatial risk of malaria through probability distribution of Anopheles maculipennis s.l. and imported cases

dc.contributor.author
Taheri, Shirin
dc.contributor.author
González, Mikel Alexander
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Ruiz-López, María José
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Magallanes, Sergio
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Delacour-Estrella, Sarah
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Lucientes, Javier
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Bueno-Marí, Rubén
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Martínez-de la Puente, Josué
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Bravo-Barriga, Daniel
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Frontera, Eva
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Polina, Alejandro
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Martinez-Barciela, Yasmina
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Pereira, José Manuel
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Garrido, Josefina
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Aranda, Carles
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Marzal, Alfonso
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Ruiz-Arrondo, Ignacio
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Oteo, José Antonio
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Ferraguti, Martina
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Gutíerrez-López, Rafael
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Estrada, Rosa
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Miranda, Miguel Ángel
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Barceló, Carlos
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Morchón, Rodrigo
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Montalvo, Tomas
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Gangoso, Laura
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Goiri, Fátima
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García-Pérez, Ana L.
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Ruiz, Santiago
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Fernandez-Martinez, Beatriz
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Gómez-Barroso, Diana
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Figuerola, Jordi
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Producció Animal
dc.date.accessioned
2025-10-22T11:08:05Z
dc.date.available
2025-10-22T11:08:05Z
dc.date.issued
2024-05-02
dc.identifier.citation
Taheri, Shirin, María Rosa Luengo González, María José Ruiz‐López, Sergio Magallanes, Sarah Delacour, Javier Lucientes, Rubén Bueno‐Marí, et al. 2024. “Modeling the Spatial Risk of Malaria Through Probability Distribution of Anopheles Maculipennis s.l. And Imported Cases.” Emerging Microbes & Infections 13 (1): 2343911. doi:10.1080/22221751.2024.2343911
dc.identifier.issn
2222-1751
dc.identifier.uri
https://hdl.handle.net/20.500.12327/2974
dc.description.abstract
Malaria remains one of the most important infectious diseases globally due to its high incidence and mortality rates. The influx of infected cases from endemic to non-endemic malaria regions like Europe has resulted in a public health concern over sporadic local outbreaks. This is facilitated by the continued presence of competent Anopheles vectors in nonendemic countries. We modelled the potential distribution of the main malaria vector across Spain using the ensemble of eight modelling techniques based on environmental parameters and the Anopheles maculipennis s.l. presence/absence data collected from 2000 to 2020. We then combined this map with the number of imported malaria cases in each municipality to detect the geographic hot spots with a higher risk of local malaria transmission. The malaria vector occurred preferentially in irrigated lands characterized by warm climate conditions and moderate annual precipitation. Some areas surrounding irrigated lands in northern Spain (e.g. Zaragoza, Logroño), mainland areas (e.g. Madrid, Toledo) and in the South (e.g. Huelva), presented a significant likelihood of A. maculipennis s.l. occurrence, with a large overlap with the presence of imported cases of malaria. While the risk of malaria re-emergence in Spain is low, it is not evenly distributed throughout the country. The four recorded local cases of mosquito-borne transmission occurred in areas with a high overlap of imported cases and mosquito presence. Integrating mosquito distribution with human incidence cases provides an effective tool for the quantification of large-scale geographic variation in transmission risk and pinpointing priority areas for targeted surveillance and prevention.
dc.description.sponsorship
MCIN/AEI through the European Regional Development Fund (SUMHAL, LifeWatch-2019-09-CSIC-4, POPE 2014-2020) and PLEC2021-007968 project NEXTHREAT MCIN/AEI/10.13039/2011000110333 and European Union Next Generation EU/PRTR funds, CIBER Epidemiología y Salud Pública and La Caixa Foundation through the project ARBOPREVENT (HR22-00123). Part of the samples used for the analyses were provided from studies financed from projects IB16121 and IB16135 from the Extremadura Regional Government, from Ayudas Fundación BBVA a Equipos de Investigación Científica 2019 (PR (19_ECO_0070)). MF is currently funded by a Ramón y Cajal postdoctoral contract (RYC2021- 031613-I) from the Spanish Ministry of Science and Innovation (MICINN). M.J.R.L received support from the Agencia Estatal de Investigación (project PID2020-118921RJ-100 funded by MCIN/AEI/10.13039/501100011033). We thank the contribution of all professionals participating in the Spanish Surveillance System (RENAVE) and those collaboration with the entomological surveys.
dc.format.extent
11
dc.language.iso
eng
dc.publisher
Taylor and Francis
dc.relation.ispartof
Emerging Microbes & Infections
dc.rights
Attribution 4.0 International
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Modelling the spatial risk of malaria through probability distribution of Anopheles maculipennis s.l. and imported cases
dc.type
info:eu-repo/semantics/article
dc.subject.udc
619
dc.description.version
info:eu-repo/semantics/publishedVersion
dc.embargo.terms
cap
dc.relation.projectID
EC/LifeWatch ERIC/LifeWatch-2019-09-CSIC-4/EU/Sustainability for Mediterranean Hotspots in Andalusia Integrating LifeWatch ERIC/SUMHAL
dc.relation.projectID
ISCIII/Programa Estatal de I+D+I orientada a los retos de la sociedad/PLEC2021-007968/ES/Development of New Technologies to Track Emerging Infectious Threats in Wildlife and the Environment/NEXTHREAT
dc.relation.projectID
MICINN/Programa Estatal para desarrollar, atraer y retener talento/RYC2021-031613-I/ES/ /
dc.relation.projectID
MCIN/Programa Estatal de generación del conocimiento y fortalecimiento científico y tecnológico del sistema I+D+I y Programa Estatal de I+D+I orientada a los retos de la sociedad/PID2020-118921RJ-I00/ES/ /
dc.relation.projectID
FEDER/ / /EU/ /
dc.identifier.doi
https://doi.org/10.1080/22221751.2024.2343911
dc.rights.accessLevel
info:eu-repo/semantics/openAccess
dc.contributor.group
Sanitat Animal


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