Dynamic risk model for Rift Valley fever outbreaks in Kenya based on climate and disease outbreak data

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cg.authorship.typesCGIAR and developing country instituteen
cg.contributor.affiliationKenya Meteorological Serviceen
cg.contributor.affiliationMasinde Muliro University of Science and Technologyen
cg.contributor.affiliationInternational Livestock Research Instituteen
cg.contributor.crpAgriculture for Nutrition and Healthen
cg.coverage.countryKenyaen
cg.coverage.iso3166-alpha2KEen
cg.coverage.regionAfricaen
cg.coverage.regionEastern Africaen
cg.creator.identifierBernard Bett: 0000-0001-9376-2941en
cg.howPublishedFormally Publisheden
cg.identifier.doihttps://doi.org/10.4081/gh.2016.377en
cg.identifier.urlhttp://geospatialhealth.net/index.php/gh/article/view/377en
cg.isijournalISI Journalen
cg.issn1827-1987en
cg.issue2en
cg.journalGeospatial Healthen
cg.reviewStatusPeer Reviewen
cg.subject.ilriANIMAL DISEASESen
cg.subject.ilriDISEASE CONTROLen
cg.subject.ilriLIVESTOCKen
cg.subject.ilriRVFen
cg.subject.ilriZOONOTIC DISEASESen
cg.volume11en
dc.contributor.authorGikungu, D.en
dc.contributor.authorNeyole, E.en
dc.contributor.authorMuita, R.en
dc.contributor.authorWakhungu, Judi W.en
dc.contributor.authorSiamba, D.en
dc.contributor.authorBett, Bernard K.en
dc.date.accessioned2016-06-06T05:04:10Zen
dc.date.available2016-06-06T05:04:10Zen
dc.identifier.urihttps://hdl.handle.net/10568/75605
dc.titleDynamic risk model for Rift Valley fever outbreaks in Kenya based on climate and disease outbreak dataen
dcterms.abstractRift Valley fever (RVF) is a mosquito-borne viral zoonotic disease that occurs throughout sub-Saharan Africa, Egypt and the Arabian Peninsula, with heavy impact in affected countries. Outbreaks are episodic and related to climate variability, especially rainfall and flooding. Despite great strides towards better prediction of RVF epidemics, there is still no observed climate data-based warning system with sufficient lead time for appropriate response and mitigation. We present a dynamic risk model based on historical RVF outbreaks and observed meteorological data. The model uses 30-year data on rainfall, temperature, relative humidity, normalised difference vegetation index and sea surface temperature data as predictors. Our research on RVF focused on Garissa, Murang’a and Kwale counties in Kenya using a research design based on a correlational, experimental, and evaluational approach. The weather data were obtained from the Kenya Meteorological Department while the RVF data were acquired from International Livestock Research Institute, and the Department of Veterinary Services. Performance of the model was evaluated by using the first 70% of the data for calibration and the remaining 30% for validation. The assessed components of the model accurately predicted already observed RVF events. The Brier score for each of the models (ranging from 0.007 to 0.022) indicated high skill. The coefficient of determination (R2) was higher in Garissa (0.66) than in Murang’a (0.21) and Kwale (0.16). The discrepancy was attributed to data distribution differences and varying ecosystems. The model outputs should complement existing early warning systems to detect risk factors that predispose for RVF outbreaks.en
dcterms.accessRightsOpen Accessen
dcterms.audienceScientistsen
dcterms.available2016-05-31en
dcterms.bibliographicCitationGikungu, D., Wakhungu, J., Siamba, D., Neyole, E., Muita, R. and Bett, B. 2016. Dynamic risk model for Rift Valley fever outbreaks in Kenya based on climate and disease outbreak data. Geospatial Health 11(2): 95-103.en
dcterms.extentp. 95-103en
dcterms.issued2016-05-31en
dcterms.languageenen
dcterms.licenseCC-BY-NC-4.0en
dcterms.publisherPAGEPress Publicationsen
dcterms.subjectanimal diseasesen
dcterms.subjectzoonosesen
dcterms.typeJournal Articleen

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