Spatial risk assessment of Rift Valley fever in Senegal.
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.
Clements
Archie C A
AC
Epidemiology Division, Department of Veterinary Clinical Sciences, Royal Veterinary College, Hatfield, Hertfordshire, United Kingdom. a.clements@sph.uq.edu.au
Pfeiffer
Dirk U
DU
Martin
Vincent
V
Pittliglio
Claudia
C
Best
Nicky
N
Thiongane
Yaya
Y
D000818:Animals; D001499:Bayes Theorem; D002417:Cattle; D002418:Cattle Diseases / Q000453:epidemiology; D003430:Cross-Sectional Studies; D015511:Goat Diseases / Q000453:epidemiology; D006041:Goats; D009500:Neutralization Tests / Q000662:veterinary; D015995:Prevalence; D011891:Rain; D012295:Rift Valley Fever / Q000453:epidemiology / Q000662:veterinary*; D012296:Rift Valley fever virus / Q000302:isolation & purification*; D018570:Risk Assessment; D012307:Risk Factors; D012621:Seasons; D012675:Senegal / Q000453:epidemiology; D018571:Sentinel Surveillance / Q000662:veterinary*; D012756:Sheep; D012757:Sheep Diseases / Q000453:epidemiology; D013696:Temperature