title: Predictive Factors and Risk Mapping for Rift Valley Fever Epidemics in Kenya.
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).
  
  
  CONCLUSIONS
  RVF risk map serves as an important tool for developing and deploying prevention and control measures against the disease.
authors.affiliation: Global Disease Detection Division, United States Centers for Disease Control and Prevention-Kenya, Nairobi, Kenya.
authors.forename: Peninah M
authors.initials: PM
authors.lastname: Munyua
authors.affiliation: Ministry of Agriculture, Livestock and Fisheries, Nairobi, Kenya.
authors.forename: R Mbabu
authors.initials: RM
authors.lastname: Murithi
authors.affiliation: Ministry of Agriculture, Livestock and Fisheries, Nairobi, Kenya.
authors.forename: Peter
authors.initials: P
authors.lastname: Ithondeka
authors.affiliation: Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
authors.forename: Allen
authors.initials: A
authors.lastname: Hightower
authors.affiliation: Paul G. Allen School for Global Animal Health, Washington State University, Pullman, Washington, United States of America.
authors.forename: Samuel M
authors.initials: SM
authors.lastname: Thumbi
authors.affiliation: Ministry of Health, Nairobi, Kenya.
authors.forename: Samuel A
authors.initials: SA
authors.lastname: Anyangu
authors.affiliation: International Livestock Research Institute, Nairobi, Kenya.
authors.forename: Jusper
authors.initials: J
authors.lastname: Kiplimo
authors.affiliation: International Livestock Research Institute, Nairobi, Kenya.
authors.forename: Bernard
authors.initials: B
authors.lastname: Bett
authors.affiliation: Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands.
authors.forename: Anton
authors.initials: A
authors.lastname: Vrieling
authors.affiliation: Global Disease Detection Division, United States Centers for Disease Control and Prevention-Kenya, Nairobi, Kenya.
authors.forename: Robert F
authors.initials: RF
authors.lastname: Breiman
authors.affiliation: Global Disease Detection Division, United States Centers for Disease Control and Prevention-Kenya, Nairobi, Kenya.
authors.forename: M Kariuki
authors.initials: MK
authors.lastname: Njenga
mesh_terms: D000034:Abortion, Veterinary / Q000453:epidemiology / Q000209:etiology; D000330:Aedes / Q000821:virology; D000531:Altitude; D000818:Animals; D002020:Buffaloes; D002162:Camelus; D002417:Cattle; D002418:Cattle Diseases / Q000453:epidemiology; D002980:Climate; D004196:Disease Outbreaks; D004463:Ecology; D005260:Female; D062305:Geographic Mapping; D015511:Goat Diseases / Q000453:epidemiology; D006041:Goats; D006801:Humans; D007303:Insect Vectors / Q000821:virology; D007630:Kenya / Q000453:epidemiology; D008297:Male; D011159:Population Surveillance; D011247:Pregnancy; D011891:Rain; D012295:Rift Valley Fever / Q000453:epidemiology*; D012307:Risk Factors; D012756:Sheep; D012757:Sheep Diseases / Q000453:epidemiology; D012987:Soil / Q000145:classification
chemical_list: D012987:Soil