We analyzed the extent of livestock involvement in the latest Rift Valley fever (RVF) outbreak in Kenya that started in December 2006 and continued until June 2007. When compared with previous RVF outbreaks in the country, the 2006-07 outbreak was the most extensive in cattle, sheep, goats, and camels affecting thousands of animals in 29 of 69 administrative districts across six of the eight provinces. This contrasted with the distribution of approximately 700 human RVF cases in the country, where over 85% of these cases were located in four districts; Garissa and Ijara districts in Northeastern Province, Baringo district in Rift Valley Province, and Kilifi district in Coast Province. Analysis of livestock and human data suggests that livestock infections occur before virus detection in humans, as supported by clustering of human RVF cases around livestock cases in Baringo district. The highest livestock morbidity and mortality rates were recorded in Garissa and Baringo districts, the same districts that recorded a high number of human cases. The districts that reported RVF in livestock for the first time in 2006/07 included Kitui, Tharaka, Meru South, Meru central, Mwingi, Embu, and Mbeere in Eastern Province, Malindi and Taita taveta in Coast Province, Kirinyaga and Murang'a in Central Province, and Baringo and Samburu in Rift Valley Province, indicating that the disease was occurring in new regions in the country.

Since Kenya first reported Rift Valley fever (RVF)-like disease in livestock in 1912, the country has reported the most frequent epizootics of RVF disease. To determine the pattern of disease spread across the country after its introduction in 1912, and to identify regions vulnerable to the periodic epizootics, annual livestock disease records at the Department of Veterinary Services from 1910 to 2007 were analysed in order to document the number and location of RVF-infected livestock herds. A total of 38/69 (55%) administrative districts in the country had reported RVF epizootics by the end of 2007. During the 1912-1950 period, the disease was confined to a district in Rift Valley province that is prone to flooding and where livestock were raised in proximity with wildlife. Between 1951 and 2007, 11 national RVF epizootics were recorded with an average inter-epizootic period of 3·6 years (range 1-7 years); in addition, all epizootics occurred in years when the average annual rainfall increased by more than 50% in the affected districts. Whereas the first two national epizootics in 1951 and 1955 were confined to eight districts in the Rift Valley province, there was a sustained epizootic between 1961 and 1964 that spread the virus to over 30% of the districts across six out of eight provinces. The Western and Nyanza provinces, located on the southwestern region of the country, had never reported RVF infections by 2007. The probability of a district being involved in a national epizootic was fivefold higher (62%) in districts that had previously reported disease compared to districts that had no prior disease activity (11%). These findings suggests that once introduced into certain permissive ecologies, the RVF virus becomes enzootic, making the region vulnerable to periodic epizootics that were probably precipitated by amplification of resident virus associated with heavy rainfall and flooding.

BackgroundTo-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.MethodsExposure 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.ResultsThe 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).ConclusionRVF risk map serves as an important tool for developing and deploying prevention and control measures against the disease.

A One Health (OH) approach that integrates human,animal and environmental approaches to management of zoonotic diseases has gained momentum in the last decadeas part of a strategy to prevent and control emerging infectious diseases. However, there are few examples of howan OH approach can be established in a country. Kenya establishment of an OH office, referred to asthe Zoonotic Disease Unit (ZDU) in 2011. The ZDU bridges theanimal and human health sectors with a senior epidemiologist deployed from each ministry; and agoal of maintaining collaboration at the animal and human health interface towards better prevention and control of zoonoses. The country is adding an ecologist to the ZDU to ensure that environmental risks are adequately addressed in emerging disease control.

Rickettsiae are obligate intracellular bacteria that cause zoonotic and human diseases. Arthropod vectors, such as fleas, mites, ticks, and lice, transmit rickettsiae to vertebrates during blood meals. In humans, the disease can be life threatening. This study was conducted amidst rising reports of rickettsioses among travelers to Kenya. Ticks and whole blood were collected from domestic animals presented for slaughter at major slaughterhouses in Nairobi and Mombasa that receive animals from nearly all counties in the country. Blood samples and ticks were collected from 1019 cattle, 379 goats, and 299 sheep and were screened for rickettsiae by a quantitative PCR (qPCR) assay (Rick17b) using primers and probe that target the genus-specific 17-kD gene (htrA). The ticks were identified using standard taxonomic keys. All Rick17b-positive tick DNA samples were amplified and sequenced with primers sets that target rickettsial outer membrane protein genes (ompA and ompB) and the citrate-synthase encoding gene (gltA). Using the Rick17b qPCR, rickettsial infections in domestic animals were found in 25/32 counties sampled (78.1% prevalence). Infection rates were comparable in cattle (16.3%) and sheep (15.1%) but were lower in goats (7.1%). Of the 596 ticks collected, 139 had rickettsiae (23.3%), and the detection rates were highest in Amblyomma (62.3%; n=104), then Rhipicephalus (45.5%; n=120), Hyalomma (35.9%; n=28), and Boophilus (34.9%; n=30). Following sequencing, 104 out of the 139 Rick17b-positive tick DNA had good reverse and forward sequences for the 3 target genes. On querying GenBank with the generated consensus sequences, homologies of 92-100% for the following spotted fever group (SFG) rickettsiae were identified: Rickettsia africae (93.%, n=97), Rickettsia aeschlimannii (1.9%, n=2), Rickettsia mongolotimonae (0.96%, n=1), Rickettsia conorii subsp. israelensis (0.96%, n=1), Candidatus Rickettsia kulagini (0.96% n=1), and Rickettsia spp. (1.9% n=2). In conclusion, molecular methods were used in this study to detect and identify rickettsial infections in domestic animals and ticks throughout Kenya.