Association of ecological factors with Rift Valley fever occurrence and mapping of risk zones in Kenya. (May 2016)

• Record Type: Journal Article
• Authors: Mosomtai, Gladys; Evander, Magnus; Sandström, Per; Ahlm, Clas; Sang, Rosemary; Hassan, Osama Ahmed; Affognon, Hippolyte; Landmann, Tobias
• Is Part Of: International journal of infectious diseases. Volume 46(2016:May)
• Page Start: 49
• Page End: 55
• Publication Date: 2016-05
• Subjects: Rift Valley fever -- Evapotranspiration -- Normalized difference vegetation index -- Animal density -- Disease mapping; Communicable diseases -- Periodicals; Communicable Diseases -- Periodicals; Communicable diseases; Periodicals; Electronic journals; 616.9
• DOI: 10.1016/j.ijid.2016.03.013
• Languages: English
• ISSNs: 1201-9712

Abstract

Graphical abstract: Highlights: The first spatially explicit Rift Valley fever risk map was produced for a study region in Kenya. Remote sensing-based variables contributed significantly to the mapping model. Statistically based variable selection improved the meaningfulness of the mapping result. Summary: Objective: Rift Valley fever (RVF) is a mosquito-borne infection with great impact on animal and human health. The objectives of this study were to identify ecological factors that explain the risk of RVF outbreaks in eastern and central Kenya and to produce a spatially explicit risk map. Methods: The sensitivity of seven selected ecological variables to RVF occurrence was assessed by generalized linear modelling (GLM). Vegetation seasonality variables (from normalized difference vegetation index (NDVI) data) and 'evapotranspiration' (ET) (metrics) were obtained from 0.25–1 km MODIS satellite data observations; 'livestock density' (N/km 2 ), 'elevation' (m), and 'soil ratio' (fraction of all significant soil types within a certain county as a function of the total area of that county) were used as covariates. Results: 'Livestock density', 'small vegetation integral', and the second principal component of ET were the most significant determinants of RVF occurrence in Kenya (all p ≤ 0.01), with high RVF risk areas identified in the counties of Tana River, Garissa, Isiolo, and Lamu. Conclusions: Wet soil fluxes measured with ET and vegetation seasonality variables could be used to map RVF risk zones on a sub-regional scale. Future outbreaks could be better managed if relevant RVF variables are integrated into early warning systems.