Publication:
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.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.authorGutiérrez-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.authorGomez-Barroso, Diana
dc.contributor.authorFiguerola, Jordi
dc.contributor.funderAgencia Estatal de Investigación (España)es_ES
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es_ES
dc.contributor.funderUnión Europea. Fondo Europeo de Desarrollo Regional (FEDER/ERDF)es_ES
dc.contributor.funderCentro de Investigación Biomédica en Red - CIBERESP (Epidemiología y Salud Pública)es_ES
dc.contributor.funderFundación La Caixaes_ES
dc.contributor.funderGovernment of Extremadura (España)es_ES
dc.contributor.funderFundación BBVAes_ES
dc.date.accessioned2024-05-14T12:22:55Z
dc.date.available2024-05-14T12:22:55Z
dc.date.issued2024-12
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.es_ES
dc.description.peerreviewedes_ES
dc.description.sponsorshipMCIN/AEI through the European Regional Development Fund (SUMHAL, Life Watch-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 ARBOPRE-VENT (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).es_ES
dc.format.number1es_ES
dc.format.page2343911es_ES
dc.format.volume13es_ES
dc.identifier.citationEmerg Microbes Infect. 2024 Dec;13(1):2343911.es_ES
dc.identifier.doi10.1080/22221751.2024.2343911es_ES
dc.identifier.e-issn2222-1751es_ES
dc.identifier.journalEmerging microbes & infectionses_ES
dc.identifier.pubmedID38618930es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12105/19424
dc.language.isoenges_ES
dc.publisherTaylor & Francises_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/PID2020-118921RJ-100es_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/RYC2021-031613-Ies_ES
dc.relation.publisherversionhttps://doi.org/10.1080/22221751.2024.2343911es_ES
dc.repisalud.centroISCIII::Centro Nacional de Epidemiologíaes_ES
dc.repisalud.centroISCIII::Centro Nacional de Microbiologíaes_ES
dc.repisalud.institucionISCIIIes_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.licenseAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectPaludismes_ES
dc.subjectPathogeographyes_ES
dc.subjectSpatial epidemiologyes_ES
dc.subjectSpecies distribution modellinges_ES
dc.subjectRisk mapses_ES
dc.subjectVector-borne diseaseses_ES
dc.subject.meshAnopheleses_ES
dc.subject.meshMalariaes_ES
dc.subject.meshMosquito Vectorses_ES
dc.subject.meshAnimalses_ES
dc.subject.meshSpaines_ES
dc.subject.meshHumanses_ES
dc.subject.meshCommunicable Diseases, Importedes_ES
dc.subject.meshIncidencees_ES
dc.titleModelling the spatial risk of malaria through probability distribution of Anopheles maculipennis s.l. and imported caseses_ES
dc.typeresearch articlees_ES
dc.type.hasVersionVoRes_ES
dspace.entity.typePublication
relation.isAuthorOfPublication8a0888de-405c-48fe-8afe-0e96730a5320
relation.isAuthorOfPublicationdffea7c1-0d44-4b8a-aa55-53669a24a097
relation.isAuthorOfPublication.latestForDiscovery8a0888de-405c-48fe-8afe-0e96730a5320

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