Predictive factors and risk mapping for Rift Valley fever epidemics in Kenya
Background 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 associate...
| Main Authors: | , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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Public Library of Science
2016
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| Online Access: | https://hdl.handle.net/10568/71002 |
| _version_ | 1855519828423475200 |
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| author | Munyua, P.M. Murithi, R.M. Ithondeka, P.M. Hightower, A. Thumbi, Samuel M. Anyangu, S.A. Kiplimo, Jusper Ronoh Bett, Bernard K. Vrieling, A. Breiman, R.F. Njenga, M.K. |
| author_browse | Anyangu, S.A. Bett, Bernard K. Breiman, R.F. Hightower, A. Ithondeka, P.M. Kiplimo, Jusper Ronoh Munyua, P.M. Murithi, R.M. Njenga, M.K. Thumbi, Samuel M. Vrieling, A. |
| author_facet | Munyua, P.M. Murithi, R.M. Ithondeka, P.M. Hightower, A. Thumbi, Samuel M. Anyangu, S.A. Kiplimo, Jusper Ronoh Bett, Bernard K. Vrieling, A. Breiman, R.F. Njenga, M.K. |
| author_sort | Munyua, P.M. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Background 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. |
| format | Journal Article |
| id | CGSpace71002 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2016 |
| publishDateRange | 2016 |
| publishDateSort | 2016 |
| publisher | Public Library of Science |
| publisherStr | Public Library of Science |
| record_format | dspace |
| spelling | CGSpace710022025-01-27T15:00:52Z Predictive factors and risk mapping for Rift Valley fever epidemics in Kenya Munyua, P.M. Murithi, R.M. Ithondeka, P.M. Hightower, A. Thumbi, Samuel M. Anyangu, S.A. Kiplimo, Jusper Ronoh Bett, Bernard K. Vrieling, A. Breiman, R.F. Njenga, M.K. swine zoonoses Background 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. 2016-01-25 2016-02-11T20:11:44Z 2016-02-11T20:11:44Z Journal Article https://hdl.handle.net/10568/71002 en Open Access Public Library of Science Munyua, 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. |
| spellingShingle | swine zoonoses Munyua, P.M. Murithi, R.M. Ithondeka, P.M. Hightower, A. Thumbi, Samuel M. Anyangu, S.A. Kiplimo, Jusper Ronoh Bett, Bernard K. Vrieling, A. Breiman, R.F. Njenga, M.K. Predictive factors and risk mapping for Rift Valley fever epidemics in Kenya |
| title | Predictive factors and risk mapping for Rift Valley fever epidemics in Kenya |
| title_full | Predictive factors and risk mapping for Rift Valley fever epidemics in Kenya |
| title_fullStr | Predictive factors and risk mapping for Rift Valley fever epidemics in Kenya |
| title_full_unstemmed | Predictive factors and risk mapping for Rift Valley fever epidemics in Kenya |
| title_short | Predictive factors and risk mapping for Rift Valley fever epidemics in Kenya |
| title_sort | predictive factors and risk mapping for rift valley fever epidemics in kenya |
| topic | swine zoonoses |
| url | https://hdl.handle.net/10568/71002 |
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