The development of linear regression models using environmental variables to explain the spatial distribution of Fasciola hepatica infection in dairy herds in England and Wales.

Fasciolosis caused by Fasciola hepatica is a major cause of economic loss to the agricultural community worldwide as a result of morbidity and mortality in livestock. Spatial models developed with the aid of Geographic Information Systems (GIS) can be used to develop risk maps for fasciolosis for use in the formulation of disease control programmes. Here we investigate the spatial epidemiology of F. hepatica in dairy herds in England and Wales and develop linear regression models to explain observed patterns of exposure at a small spatial unit, the postcode area. Exposure data used for the analysis were taken from an earlier study of F. hepatica infection, performed in the winter of 2006/7. Climatic, environmental, soil, livestock and pasture variables were considered as potential predictors. The performance of models that used climate variables for 5 years average data, contemporary data and a combination of both for England and Wales, and for England only, was compared. All models explained over 70% of the variation in the prevalence of exposure. The best performing models were those built using 5 year average and contemporary weather data. However, the fit of these models was only slightly better than the fit of models using weather data from one time period only. Rainfall was a consistent predictor in all models. Other model covariates included temperature, the negative predictors of soil pH and slope and the positive predictors of poor quality land, as determined by the Agricultural Land Classification, and very fine sand content of soil. Choroplethic risk maps showed a good match between the observed F. hepatica exposure values and exposure values fitted by the models. The development of these detailed spatial models is the first step towards the development of a spatially specific, temporal forecasting system for liver fluke in the United Kingdom.