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dc.contributor.authorTaheri, Shirin
dc.contributor.authorGonzález, Mikel Alexander
dc.contributor.authorRuiz-López, María José
dc.contributor.authorMagallanes, Sergio
dc.contributor.authorDelacour-Estrella, Sarah
dc.contributor.authorLucientes, Javier
dc.contributor.authorBueno-Marí, Rubén
dc.contributor.authorMartínez-de la Puente, Josué
dc.contributor.authorBravo-Barriga, Daniel
dc.contributor.authorFrontera, Eva
dc.contributor.authorPolina, Alejandro
dc.contributor.authorMartinez-Barciela, Yasmina
dc.contributor.authorPereira, José Manuel
dc.contributor.authorGarrido, Josefina
dc.contributor.authorAranda, Carles
dc.contributor.authorMarzal, Alfonso
dc.contributor.authorRuiz-Arrondo, Ignacio
dc.contributor.authorOteo, José Antonio
dc.contributor.authorFerraguti, Martina
dc.contributor.authorGutíerrez-López, Rafael
dc.contributor.authorEstrada, Rosa
dc.contributor.authorMiranda, Miguel Ángel
dc.contributor.authorBarceló, Carlos
dc.contributor.authorMorchón, Rodrigo
dc.contributor.authorMontalvo, Tomas
dc.contributor.authorGangoso, Laura
dc.contributor.authorGoiri, Fátima
dc.contributor.authorGarcía-Pérez, Ana L.
dc.contributor.authorRuiz, Santiago
dc.contributor.authorFernandez-Martinez, Beatriz
dc.contributor.authorGómez-Barroso, Diana
dc.contributor.authorFiguerola, Jordi
dc.contributor.otherProducció Animalca
dc.date.accessioned2024-05-10T09:04:13Z
dc.date.available2024-05-10T09:04:13Z
dc.date.issued2024-05-02
dc.identifier.citationTaheri, 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:10.1080/22221751.2024.2343911ca
dc.identifier.issn2222-1751ca
dc.identifier.urihttp://hdl.handle.net/20.500.12327/2974
dc.description.abstractMalaria 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.ca
dc.description.sponsorshipMCIN/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.ca
dc.format.extent11ca
dc.language.isoengca
dc.publisherTaylor and Francisca
dc.relation.ispartofEmerging Microbes & Infectionsca
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleModelling the spatial risk of malaria through probability distribution of Anopheles maculipennis s.l. and imported casesca
dc.typeinfo:eu-repo/semantics/articleca
dc.description.versioninfo:eu-repo/semantics/publishedVersionca
dc.rights.accessLevelinfo:eu-repo/semantics/openAccess
dc.embargo.termscapca
dc.relation.projectIDEC/LifeWatch ERIC/LifeWatch-2019-09-CSIC-4/EU/Sustainability for Mediterranean Hotspots in Andalusia Integrating LifeWatch ERIC/SUMHALca
dc.relation.projectIDISCIII/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/NEXTHREATca
dc.relation.projectIDMICINN/Programa Estatal para desarrollar, atraer y retener talento/RYC2021-031613-I/ES/ /ca
dc.relation.projectIDMCIN/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/ /ca
dc.relation.projectIDFEDER/ / /EU/ /ca
dc.subject.udc619ca
dc.identifier.doihttps://doi.org/10.1080/22221751.2024.2343911ca
dc.contributor.groupSanitat Animalca


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