Rift Valley fever virus seroprevalence data from cattle, sheep and goats sampled in a cross sectional survey in Tana River County, Kenya (2013)

These data provide results from serological analysis carried out on serum collected from cattle (sample number = 460), goats (sample number = 949) and sheep (Sample number = 574) combined with data collected at the household and subject/animal levels at the time of serum sampling. The data collected at the household and subject/animal levels were: the total number of livestock owned by a household, altitude, geographical coordinates of the sampling sites; and breed, age, sex and body condition score of an animal. The research was carried out in irrigated and non-irrigated areas in Tana River County, Kenya. Field surveys were implemented in August to November 2013 and laboratory analyses were completed in June 2015. Serum samples were harvested from blood samples obtained from animals and screened for anti-Rift Valley Fever (RVF) virus immunoglobulin G using inhibition (enzyme-linked immunosorbent assay) ELISA immunoassay. The household data was collected using Open Data Kit (ODK) loaded into smart phones. The serological analysis was performed to determine the risk of Rift Valley Fever virus exposure in cattle, sheep and goats. The aim of the survey was to investigate whether land use change, specifically the conversion of rangeland into cropland, affected RVF exposure pattern in livestock. The data were collected by experienced researchers from the Ministry of Livestock Development Nairobi, Kenya and the International Livestock Research Institute (Kenya). This dataset is part of a wider research project, the Dynamic Drivers of Disease in Africa Consortium (DDDAC). The research was funded by NERC project no NE-J001570-1 with support from the Ecosystem Services for Poverty Alleviation Programme (ESPA). Additional funding was provided by Consultative Group on International Agricultural Research (CGIAR) Research Program Agriculture for Nutrition and Health led by International Food Policy Research Institute (IFPRI). Full details about this dataset can be found at https://doi.org/10.5285/b9756c4c-9894-4147-a260-a79067604a06