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Preferential sampling in veterinary parasitological surveillance.

Lorenzo Cecconi1, Annibale Biggeri, Laura Grisotto

  • 1Department of Statistics, Computer Science, Applications, University of Florence, Florence. lcecconi@disia.unifi.it.

Geospatial Health
|April 19, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces geostatistical models to accurately predict parasite infection risk in livestock, accounting for preferential sampling common in veterinary parasitology. The findings improve spatial risk assessment for diseases like Fasciola hepatica.

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Area of Science:

  • Veterinary Parasitology
  • Spatial Statistics
  • Epidemiology

Background:

  • Livestock prevalence surveys often use non-independent sampling designs, such as opportunistic or informative sampling.
  • Preferential sampling, where sampling locations correlate with the spatial process, can lead to misleading statistical inference if not addressed.
  • Existing veterinary parasitological surveillance commonly overlooks preferential sampling.

Purpose of the Study:

  • To develop and apply geostatistical models for predicting spatially-varying parasite infection risk.
  • To address and adjust for preferential sampling in veterinary parasitological data analysis.
  • To improve the accuracy of risk prediction for Fasciola hepatica in sheep farms.

Main Methods:

  • Specification of a two-stage hierarchical Bayesian model.
  • Incorporation of methods to adjust for preferential sampling.
  • Application to Fasciola hepatica infection data from sheep farms in Southern Italy (2013-2014).

Main Results:

  • The proposed geostatistical model successfully predicts continuous, spatially-varying parasite infection risk.
  • The model effectively adjusts for preferential sampling, providing more reliable inference.
  • Accurate risk mapping for Fasciola hepatica infection was achieved.

Conclusions:

  • Geostatistical models adjusting for preferential sampling are crucial for accurate parasitological risk assessment in livestock.
  • Ignoring preferential sampling can lead to biased results in veterinary epidemiology.
  • This approach enhances disease surveillance and control strategies.