Predictive factors and risk mapping for Rift Valley fever epidemics in Kenya

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cg.authorship.typesCGIAR and developing country instituteen_US
cg.contributor.affiliationMinistry of Agriculture, Livestock, and Fisheries, Kenyaen_US
cg.contributor.affiliationWashington State Universityen_US
cg.contributor.affiliationMinistry of Health, Kenyaen_US
cg.contributor.affiliationInternational Livestock Research Instituteen_US
cg.contributor.affiliationUniversity of Twenteen_US
cg.contributor.affiliationUnited States Centers for Disease Control and Preventionen_US
cg.contributor.crpAgriculture for Nutrition and Healthen_US
cg.coverage.countryKenyaen_US
cg.coverage.iso3166-alpha2KEen_US
cg.coverage.regionAfricaen_US
cg.coverage.regionEastern Africaen_US
cg.creator.identifierBernard Bett: 0000-0001-9376-2941en_US
cg.howPublishedFormally Publisheden_US
cg.identifier.doihttps://doi.org/10.1371/journal.pone.0144570en_US
cg.isijournalISI Journalen_US
cg.issn1932-6203en_US
cg.issue1en_US
cg.journalPLOS ONEen_US
cg.reviewStatusPeer Reviewen_US
cg.subject.ilriANIMAL DISEASESen_US
cg.subject.ilriDISEASE CONTROLen_US
cg.subject.ilriENVIRONMENTen_US
cg.subject.ilriEPIDEMIOLOGYen_US
cg.subject.ilriLIVESTOCKen_US
cg.subject.ilriRVFen_US
cg.subject.ilriZOONOTIC DISEASESen_US
cg.volume11en_US
dc.contributor.authorMunyua, P.M.en_US
dc.contributor.authorMurithi, R.M.en_US
dc.contributor.authorIthondeka, P.M.en_US
dc.contributor.authorHightower, A.en_US
dc.contributor.authorThumbi, Samuel M.en_US
dc.contributor.authorAnyangu, S.A.en_US
dc.contributor.authorKiplimo, Jusper Ronohen_US
dc.contributor.authorBett, Bernard K.en_US
dc.contributor.authorVrieling, A.en_US
dc.contributor.authorBreiman, R.F.en_US
dc.contributor.authorNjenga, M.K.en_US
dc.date.accessioned2016-02-11T20:11:44Zen_US
dc.date.available2016-02-11T20:11:44Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/71002en_US
dc.titlePredictive factors and risk mapping for Rift Valley fever epidemics in Kenyaen_US
dcterms.abstractBackground To-date, Rift Valley fever (RVF) outbreaks have occurred in 38 of the 69 administrative districts in Kenya. Using surveillance records collected between 1951 and 2007, we determined the risk of exposure and outcome of an RVF outbreak, examined the ecological and climatic factors associated with the outbreaks, and used these data to develop an RVF risk map for Kenya. Methods Exposure to RVF was evaluated as the proportion of the total outbreak years that each district was involved in prior epizootics, whereas risk of outcome was assessed as severity of observed disease in humans and animals for each district. A probability-impact weighted score (1 to 9) of the combined exposure and outcome risks was used to classify a district as high (score ≥ 5) or medium (score ≥2 - <5) risk, a classification that was subsequently subjected to expert group analysis for final risk level determination at the division levels (total = 391 divisions). Divisions that never reported RVF disease (score < 2) were classified as low risk. Using data from the 2006/07 RVF outbreak, the predictive risk factors for an RVF outbreak were identified. The predictive probabilities from the model were further used to develop an RVF risk map for Kenya. Results The final output was a RVF risk map that classified 101 of 391 divisions (26%) located in 21 districts as high risk, and 100 of 391 divisions (26%) located in 35 districts as medium risk and 190 divisions (48%) as low risk, including all 97 divisions in Nyanza and Western provinces. The risk of RVF was positively associated with Normalized Difference Vegetation Index (NDVI), low altitude below 1000m and high precipitation in areas with solonertz, luvisols and vertisols soil types (p <0.05). Conclusion RVF risk map serves as an important tool for developing and deploying prevention and control measures against the disease.en_US
dcterms.accessRightsOpen Accessen_US
dcterms.audienceScientistsen_US
dcterms.available2016-01-25en_US
dcterms.bibliographicCitationMunyua, P.M., Murithi, R.M., Ithondeka, P., Hightower, A., Thumbi, S.M., Anyangu, S.A., Kiplimo, J., Bett, B., Vrieling, A., Breiman, R.F. and Njenga, M.K. 2016. Predictive factors and risk mapping for Rift Valley fever epidemics in Kenya. PLoS ONE 11(1): e0144570.en_US
dcterms.issued2016-01-25en_US
dcterms.languageenen_US
dcterms.licenseCC0-1.0en_US
dcterms.publisherPublic Library of Scienceen_US
dcterms.subjectswineen_US
dcterms.subjectzoonosesen_US
dcterms.typeJournal Articleen_US

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Learning the lessons of Rift Valley fever: Improved detection and mitigation of outbreaks