Preferential sampling in veterinary parasitological surveillance

Submitted: 22 September 2015
Accepted: 19 February 2016
Published: 18 April 2016
Abstract Views: 1742
PDF: 1327
HTML: 1031
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.


In parasitological surveillance of livestock, prevalence surveys are conducted on a sample of farms using several sampling designs. For example, opportunistic surveys or informative sampling designs are very common. Preferential sampling refers to any situation in which the spatial process and the sampling locations are not independent. Most examples of preferential sampling in the spatial statistics literature are in environmental statistics with focus on pollutant monitors, and it has been shown that, if preferential sampling is present and is not accounted for in the statistical modelling and data analysis, statistical inference can be misleading. In this paper, working in the context of veterinary parasitology, we propose and use geostatistical models to predict the continuous and spatially-varying risk of a parasite infection. Specifically, breaking with the common practice in veterinary parasitological surveillance to ignore preferential sampling even though informative or opportunistic samples are very common, we specify a two-stage hierarchical Bayesian model that adjusts for preferential sampling and we apply it to data on Fasciola hepatica infection in sheep farms in Campania region (Southern Italy) in the years 2013-2014.



PlumX Metrics


Download data is not yet available.


Supporting Agencies

The research leading to these results has received funding from the European Union Seventh Framework Programme FP7-KBBE-2011-5 under grant agreement no 288975 and University of Florence, ex 60%

How to Cite

Cecconi, L., Biggeri, A., Grisotto, L., Berrocal, V., Rinaldi, L., Musella, V., Cringoli, G., & Catelan, D. (2016). Preferential sampling in veterinary parasitological surveillance. Geospatial Health, 11(1).

List of Cited By :

Crossref logo