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

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cg.authorship.typesCGIAR and advanced research instituteen_US
cg.contributor.affiliationUniversity College Londonen_US
cg.contributor.affiliationUniversity of Cambridgeen_US
cg.contributor.affiliationPublic Health Englanden_US
cg.contributor.affiliationInternational Livestock Research Instituteen_US
cg.contributor.affiliationZoological Society of Londonen_US
cg.contributor.crpAgriculture for Nutrition and Healthen_US
cg.contributor.donorDepartment for International Development, United Kingdomen_US
cg.contributor.donorEconomic and Social Research Council, United Kingdomen_US
cg.contributor.donorNatural Environment Research Council, United Kingdomen_US
cg.coverage.countryKenyaen_US
cg.coverage.countrySouth Africaen_US
cg.coverage.iso3166-alpha2KEen_US
cg.coverage.iso3166-alpha2ZAen_US
cg.coverage.regionAfricaEastern Africaen_US
cg.coverage.regionSouthern Africaen_US
cg.creator.identifierBernard Bett: 0000-0001-9376-2941en_US
cg.howPublishedFormally Publisheden_US
cg.identifier.doihttps://doi.org/10.1098/rstb.2016.0165en_US
cg.isijournalISI Journalen_US
cg.issn0962-8436en_US
cg.issue1725en_US
cg.journalPhilosophical Transactions of the Royal Society Ben_US
cg.reviewStatusPeer Reviewen_US
cg.subject.ilriCLIMATE CHANGEen_US
cg.subject.ilriENVIRONMENTen_US
cg.subject.ilriEPIDEMIOLOGYen_US
cg.subject.ilriRVFen_US
cg.subject.ilriZOONOTIC DISEASESen_US
cg.volume372en_US
dc.contributor.authorRedding, D.en_US
dc.contributor.authorTiedt, S.en_US
dc.contributor.authorLo Iacono, G.en_US
dc.contributor.authorBett, Bernard K.en_US
dc.contributor.authorJones, K.en_US
dc.date.accessioned2017-06-07T07:26:13Zen_US
dc.date.available2017-06-07T07:26:13Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/81469en_US
dc.titleSpatial, seasonal and climatic predictive models of Rift Valley fever disease across Africaen_US
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_US
dcterms.accessRightsOpen Accessen_US
dcterms.audienceScientistsen_US
dcterms.available2017-06-05en_US
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_US
dcterms.issued2017-07-19en_US
dcterms.languageenen_US
dcterms.licenseCopyrighted; all rights reserveden_US
dcterms.publisherRoyal Societyen_US
dcterms.subjectclimate changeen_US
dcterms.subjectepidemiologyen_US
dcterms.subjectzoonosesen_US
dcterms.typeJournal Articleen_US

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