Spatial, seasonal and climatic predictive models of Rift Valley fever disease across Africa

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cg.authorship.typesCGIAR and advanced research instituteen
cg.contributor.affiliationUniversity College Londonen
cg.contributor.affiliationUniversity of Cambridgeen
cg.contributor.affiliationPublic Health Englanden
cg.contributor.affiliationInternational Livestock Research Instituteen
cg.contributor.affiliationZoological Society of Londonen
cg.contributor.crpAgriculture for Nutrition and Health
cg.contributor.donorDepartment for International Development, United Kingdomen
cg.contributor.donorEconomic and Social Research Council, United Kingdomen
cg.contributor.donorNatural Environment Research Council, United Kingdomen
cg.coverage.countryKenya
cg.coverage.countrySouth Africa
cg.coverage.iso3166-alpha2KE
cg.coverage.iso3166-alpha2ZA
cg.coverage.regionAfricaEastern Africa
cg.coverage.regionSouthern Africa
cg.creator.identifierBernard Bett: 0000-0001-9376-2941en
cg.howPublishedFormally Publisheden
cg.identifier.doihttps://doi.org/10.1098/rstb.2016.0165en
cg.isijournalISI Journalen
cg.issn0962-8436en
cg.issue1725en
cg.journalPhilosophical Transactions of the Royal Society Ben
cg.reviewStatusPeer Reviewen
cg.subject.ilriCLIMATE CHANGEen
cg.subject.ilriENVIRONMENTen
cg.subject.ilriEPIDEMIOLOGYen
cg.subject.ilriRVFen
cg.subject.ilriZOONOTIC DISEASESen
cg.volume372en
dc.contributor.authorRedding, D.en
dc.contributor.authorTiedt, S.en
dc.contributor.authorLo Iacono, G.en
dc.contributor.authorBett, Bernard K.en
dc.contributor.authorJones, K.en
dc.date.accessioned2017-06-07T07:26:13Zen
dc.date.available2017-06-07T07:26:13Zen
dc.identifier.urihttps://hdl.handle.net/10568/81469
dc.titleSpatial, seasonal and climatic predictive models of Rift Valley fever disease across Africaen
dcterms.abstractUnderstanding the emergence and subsequent spread of human infectious diseases is a critical global challenge, especially for high-impact zoonotic and vector-borne diseases. Global climate and land-use change are likely to alter host and vector distributions, but understanding the impact of these changes on the burden of infectious diseases is difficult. Here, we use a Bayesian spatial model to investigate environmental drivers of one of the most important diseases in Africa, Rift Valley fever (RVF). The model uses a hierarchical approach to determine how environmental drivers vary both spatially and seasonally, and incorporates the effects of key climatic oscillations, to produce a continental risk map of RVF in livestock (as a proxy for human RVF risk). We find RVF risk has a distinct seasonal spatial pattern influenced by climatic variation, with the majority of cases occurring in South Africa and Kenya in the first half of an El Nin˜ o year. Irrigation, rainfall and human population density were the main drivers of RVF cases, independent of seasonal, climatic or spatial variation. By accounting more subtly for the patterns in RVF data, we better determine the importance of underlying environmental drivers, and also make spaceand time-sensitive predictions to better direct future surveillance resources. This article is part of the themed issue ‘One Health for a changing world: zoonoses, ecosystems and human well-being’.en
dcterms.accessRightsOpen Access
dcterms.audienceScientistsen
dcterms.available2017-06-05en
dcterms.bibliographicCitationRedding, D., Tiedt, S., Lo Iacono, G., Bett, B. and Jones, K. 2017. Spatial, seasonal and climatic predictive models of Rift Valley fever disease across Africa. Philosophical Transactions of The Royal Society B. Special issue on 'One Health for a changing world: zoonoses, ecosystems and human well-being'. Philosophical Transactions of the Royal Society B 372(1725): 20160165.en
dcterms.issued2017-07-19en
dcterms.languageen
dcterms.licenseCopyrighted; all rights reserved
dcterms.publisherRoyal Societyen
dcterms.subjectclimate changeen
dcterms.subjectepidemiologyen
dcterms.subjectzoonosesen
dcterms.typeJournal Article

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