Gladys Mosomtai1, Magnus Evander2, Per Sandström3, Clas Ahlm4, Rosemary Sang1, Osama Ahmed Hassan2, Hippolyte Affognon1, Tobias Landmann5. 1. International Centre of Insect Physiology and Ecology, PO Box 30772-00100, Nairobi, Kenya. 2. Department of Clinical Microbiology, Virology, Umeå University, Umeå, Sweden. 3. Department of Forest Resource Management, Faculty of Forest Sciences, Swedish University of Agricultural Sciences, Umeå, Sweden. 4. Department of Clinical Microbiology, Infectious Diseases, Umeå University, Umeå, Sweden. 5. International Centre of Insect Physiology and Ecology, PO Box 30772-00100, Nairobi, Kenya. Electronic address: tlandmann@icipe.org.
Abstract
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 0.25-1km 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.
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 0.25-1km 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.
Authors: Gladys Mosomtai; Magnus Evander; Charles Mundia; Per Sandström; Clas Ahlm; Osama Ahmed Hassan; Olivia Wesula Lwande; Moses K Gachari; Tobias Landmann; Rosemary Sang Journal: Data Brief Date: 2017-12-06
Authors: Sheila B Agha; Miguel Alvarez; Mathias Becker; Eric M Fèvre; Sandra Junglen; Christian Borgemeister Journal: Viruses Date: 2020-12-27 Impact factor: 5.048
Authors: Assaf Anyamba; Richard Damoah; Alan Kemp; Jennifer L Small; Melinda K Rostal; Whitney Bagge; Claudia Cordel; Robert Brand; William B Karesh; Janusz T Paweska Journal: Front Vet Sci Date: 2022-01-31