Preferential sampling in veterinary parasitological surveillance

  • Lorenzo Cecconi Department of Statistics, Computer Science, Applications, University of Florence, Florence, Italy.
  • Annibale Biggeri Department of Statistics, Computer Science, Applications, University of Florence, Florence; Biostatistics Unit, Institute for Cancer Prevention and Research, Florence, Italy.
  • Laura Grisotto Department of Statistics, Computer Science, Applications, University of Florence, Florence, Italy.
  • Veronica Berrocal Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States.
  • Laura Rinaldi Department of Veterinary Medicine and Animal Productions, University of Naples Federico II, Naples, Italy.
  • Vincenzo Musella Department of Health Sciences, University of Catanzaro Magna Graecia, Catanzaro, Italy.
  • Giuseppe Cringoli Department of Veterinary Medicine and Animal Productions, University of Naples Federico II, Naples, Italy.
  • Dolores Catelan | catelan@disia.unifi.it Department of Statistics, Computer Science, Applications, University of Florence, Florence; Biostatistics Unit, Institute for Cancer Prevention and Research, Florence, Italy.

Abstract

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.

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Published
2016-04-18
Section
Original Articles
Keywords:
Preferential sampling, Veterinary parasitological surveillance, Livestock
Statistics
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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). https://doi.org/10.4081/gh.2016.412

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