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

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.authorMartínez 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.authorGutiérrez López, Rafael
dc.contributor.authorEstrada, Rosa
dc.contributor.authorMiranda, Miguel Ángel
dc.contributor.authorBarceló, Carlos
dc.contributor.authorMorchón, Rodrigo
dc.contributor.authorMontalvo, Tomás
dc.contributor.authorGangoso De La Colina, Laura Esther
dc.contributor.authorGoiri, Fátima
dc.contributor.authorGarcía Pérez, Ana L.
dc.contributor.authorRuiz, Santiago
dc.contributor.authorFernández Martínez, Beatriz
dc.contributor.authorGómez Barroso, Diana
dc.contributor.authorFiguerola, Jordi
dc.date.accessioned2024-11-25T16:46:59Z
dc.date.available2024-11-25T16:46:59Z
dc.date.issued2024
dc.descriptionMCIN/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).
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 non-endemic 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.departmentDepto. de Biodiversidad, Ecología y Evolución
dc.description.facultyFac. de Ciencias Biológicas
dc.description.refereedTRUE
dc.description.sponsorshipMinisterio de Ciencia e Innovación (España)
dc.description.sponsorshipEuropean Commission
dc.description.sponsorshipFundación La Caixa
dc.description.sponsorshipJunta de Extremadura
dc.description.sponsorshipFundación BBVA
dc.description.statuspub
dc.identifier.citationTaheri, S., González, M. A., Ruiz-López, M. J., Magallanes, S., Delacour-Estrella, S., Lucientes, J., … Figuerola, J. (2024). Modelling the spatial risk of malaria through probability distribution of Anopheles maculipennis s.l. and imported cases. Emerging Microbes & Infections, 13(1). https://doi.org/10.1080/22221751.2024.2343911
dc.identifier.doi10.1080/22221751.2024.2343911
dc.identifier.issn2222-1751
dc.identifier.officialurlhttps://doi.org/10.1080/22221751.2024.2343911
dc.identifier.relatedurlhttps://www.tandfonline.com/action/showCopyRight?scroll=top&doi=10.1080%2F22221751.2024.2343911
dc.identifier.urihttps://hdl.handle.net/20.500.14352/111039
dc.issue.number1
dc.journal.titleEmerging Microbes & Infections
dc.language.isoeng
dc.publisherTaylor & Francis
dc.relation.projectIDinfo:eu-repo/grantAgreement/MCIN//PLEC2021-007968/ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/Fundación La Caixa/HR22-00123// ARBOPREVENT
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118921RJ-100/ES
dc.rightsAttribution 4.0 Internationalen
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject.cdu616.936
dc.subject.cdu616-036.22
dc.subject.cdu574.9
dc.subject.cdu565.77
dc.subject.keywordPaludism
dc.subject.keywordPathogeography
dc.subject.keywordSpatial epidemiology
dc.subject.keywordSpecies distribution modelling
dc.subject.keywordRisk maps
dc.subject.keywordVector-borne diseases
dc.subject.ucmParasitología (Medicina)
dc.subject.ucmEcología (Biología)
dc.subject.ucmEnfermedades infecciosas
dc.subject.unesco3202 Epidemiología
dc.subject.unesco2401.12 Parasitología Animal
dc.subject.unesco2505.01 Biogeografía
dc.titleModelling the spatial risk of malaria through probability distribution of Anopheles maculipennis s.l. and imported cases
dc.typejournal article
dc.type.hasVersionVoR
dc.volume.number13
dspace.entity.typePublication
relation.isAuthorOfPublication74c62c71-1630-47ed-863f-661ae9502437
relation.isAuthorOfPublication.latestForDiscovery74c62c71-1630-47ed-863f-661ae9502437

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