Abstract
Rift Valley fever (RVF) is broadening its geographic range and is increasingly becoming a disease of global importance with potentially severe consequences for human and animal health. We conducted a spatial risk assessment of RVF in Senegal using serologic data from 16,738 animals in 211 locations. Bayesian spatial regression models were developed with interpolated seasonal rainfall, land surface temperature, distance to perennial water bodies, and time of year entered as fixed-effect variables. Average total monthly rainfall during December-February was the most important spatial predictor of risk of positive RVF serologic status. Maps derived from the models highlighted the lower Senegal River basin and the southern border regions of Senegal as high-risk areas. These risk maps are suitable for use in planning improved sentinel surveillance systems in Senegal, although further data collection is required in large areas of Senegal to better define the spatial distribution of RVF.
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