title: On the use of posterior predictive probabilities and prediction uncertainty to tailor informative sampling for parasitological surveillance in livestock.
abstract: 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 been done on the use of predictive infection probabilities at farm unit level. In this paper we show how to use predictive infection probability and related uncertainty from a Bayesian kriging model to draw a informative samples from the 8794 geo-referenced sheep farms of the Campania region (southern Italy). Parasitological data come from a first cross-sectional survey carried out to study the spatial distribution of selected helminths in sheep farms. A grid sampling was performed to select the farms for coprological examinations. Faecal samples were collected for 121 sheep farms and the presence of 21 different helminths were investigated using the FLOTAC technique. The 21 responses are very different in terms of geographical distribution and prevalence of infection. The observed prevalence range is from 0.83% to 96.69%. The distributions of the posterior predictive probabilities for all the 21 parasites are very heterogeneous. We show how the results of the Bayesian kriging model can be used to plan a second wave survey. Several alternatives can be chosen depending on the purposes of the second survey: weight by posterior predictive probabilities, their uncertainty or combining both information. The proposed Bayesian kriging model is simple, and the proposed samping strategy represents a useful tool to address targeted infection control treatments and surbveillance campaigns. It is easily extendable to other fields of research.
authors.affiliation: Department of Health Sciences, University of Catanzaro Magna Graecia, Catanzaro, Italy. Electronic address: musella@unicz.it.
authors.forename: Vincenzo
authors.initials: V
authors.lastname: Musella
authors.affiliation: Department of Veterinary Medicine and Animal Productions, University of Naples "Federico II", Naples, Italy. Electronic address: lrinaldi@unina.it.
authors.forename: Laura
authors.initials: L
authors.lastname: Rinaldi
authors.affiliation: Department of Economics, University of Genoa, Genoa, Italy. Electronic address: corrado.lagazio@unige.it.
authors.forename: Corrado
authors.initials: C
authors.lastname: Lagazio
authors.affiliation: Department of Veterinary Medicine and Animal Productions, University of Naples "Federico II", Naples, Italy. Electronic address: cringoli@unina.it.
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Cringoli
authors.affiliation: Department of Statistics, Informatics and Applications "G. Parenti", University of Florence, Florence, Italy; Biostatistics Unit, ISPO, Florence, Italy. Electronic address: abiggeri@disia.unifi.it.
authors.forename: Annibale
authors.initials: A
authors.lastname: Biggeri
authors.affiliation: Department of Statistics, Informatics and Applications "G. Parenti", University of Florence, Florence, Italy; Biostatistics Unit, ISPO, Florence, Italy. Electronic address: catelan@disia.unifi.it.
authors.forename: Dolores
authors.initials: D
authors.lastname: Catelan
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D040362:Geographic Information Systems; D006374:Helminthiasis, Animal / Q000453:epidemiology*; D007558:Italy / Q000453:epidemiology; D058751:Livestock; D008954:Models, Biological; D011159:Population Surveillance
keywords: Bayesian kriging model; Livestock; Parasitological surveillance; Posterior predictive probability; Sampling strategies