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PLoS One. 2016 Jan 25;11(1):e0144570. doi: 10.1371/journal.pone.0144570. eCollection 2016.

Predictive Factors and Risk Mapping for Rift Valley Fever Epidemics in Kenya.

Author information

1
Global Disease Detection Division, United States Centers for Disease Control and Prevention-Kenya, Nairobi, Kenya.
2
Ministry of Agriculture, Livestock and Fisheries, Nairobi, Kenya.
3
Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
4
Paul G. Allen School for Global Animal Health, Washington State University, Pullman, Washington, United States of America.
5
Ministry of Health, Nairobi, Kenya.
6
International Livestock Research Institute, Nairobi, Kenya.
7
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands.

Abstract

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.

PMID:
26808021
PMCID:
PMC4726791
DOI:
10.1371/journal.pone.0144570
[Indexed for MEDLINE]
Free PMC Article

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