On the use of posterior predictive probabilities and prediction uncertainty to tailor informative sampling for parasitological surveillance in livestock.

摘要: Model-based geostatistics and Bayesian approaches are appropriate in the context of Veterinary Epidemiology when point data have been collected by valid study designs. The aim is to predict a continuous infection risk surface. Little work has done on use predictive probabilities at farm unit level. In this paper we show how probability related uncertainty from kriging model draw informative samples 8794 geo-referenced sheep farms Campania region (southern Italy). Parasitological come first cross-sectional survey carried out spatial distribution selected helminths farms. A grid sampling was performed select for coprological examinations. Faecal were 121 presence 21 different investigated using FLOTAC technique. responses very terms geographical prevalence infection. observed range 0.83% 96.69%. distributions posterior all parasites heterogeneous. We results can be used plan second wave survey. Several alternatives chosen depending purposes survey: weight probabilities, their or combining both information. proposed simple, samping strategy represents useful tool address targeted control treatments surbveillance campaigns. It easily extendable other fields research.