Covariate selection in multivariate spatial analysis of ovine parasitic infection

Gastrointestinal (GI) strongyle and fluke infections remain one of the main constraints on health and productivity in sheep dairy production. A cross-sectional survey was conducted in 2004-2005 on ovine farms in the Campania region of southern Italy in order to evaluate the prevalence of Haemonchus contortus, Fasciola hepatica, Dicrocoelium dendriticum and Calicophoron daubneyi from among other parasitic infections. In the present work, we focused on the role of the ecological characteristics of the pasture environment while accounting for the underlying long range geographical risk pattern. Bayesian multivariate spatial statistical analysis was used. A systematic grid (10 km×10 km) sampling approach was used. Laboratory procedures were based on the FLOTAC technique to detect and count eggs of helminths. A Geographical Information System (GIS) was constructed by using environmental data layers. Data on each of these layers were then extracted for pasturing areas that were previously digitalized aerial images of the ovine farms. Bayesian multivariate statistical analyses, including improper multivariate conditional autoregressive models, were used to select covariates on a multivariate spatially structured risk surface. Out of the 121 tested farms, 109 were positive for H. contortus, 81 for D. dendriticum, 17 for C. daubneyi and 15 for F. hepatica. The statistical analysis highlighted a north-south long range spatially structured pattern. This geographical pattern is treated here as a confounder, because the main interest was in the causal role of ecological covariates at the level of each pasturing area. A high percentage of pasture and impermeable soil were strong predictors of F. hepatica risk and a high percentage of wood was a strong predictor of C. daubneyi. A high percentage of wood, rocks and arable soil with sparse trees explained the spatial distribution of D. dendriticum. Sparse vegetation, river, mixed soil and permeable soil explained the spatial distribution of the H. contortus. Bayesian multivariate spatial analysis of parasitic infections with covariates from remote sensing at a very small geographical level allowed us to identify relevant risk predictors. All the covariates selected are consistent with the life cycles of the helminths investigated. This research showed the utility of appropriate GIS-driven surveillance systems. Moreover, spatial features can be used to tailor sampling design where the sampling fraction can be a function of remote sensing covariables.