Helmintic infections in water buffaloes on Italian farms: a spatial analysis

The present paper reports the results of a cross-sectional survey aimed at obtaining up-to-date information on the spatial distribution of different groups and/or species of helminths in water buffaloes from central Italy. Geographical information systems (GIS) and spatial analysis were used to plan the sampling procedures, to display the results as maps and to detect spatial clusters of helminths in the study area. The survey was conducted on 127 water buffalo farms, which were selected in the study area using a grid sampling approach, followed by proportional allocation. Faecal samples (n. = 1,883) collected from the 127 farms were examined using the Flotac dual technique. Gastrointestinal strongyles were the most frequent helminths (33.1%) on the tested farms, followed by the liver fluke <em>Fasciola hepatica</em> (7.1%), the rumen fluke <em>Calicophoron daubneyi</em> (7.1%), the nematode <em>Strongyloides</em> spp. (3.1%), the lancet liver fluke <em>Dicrocoelium dendriticum</em> (2.4%) and the tapeworm <em>Moniezia</em> spp. (2.4%). In order to display the spatial distribution of the various helminths detected on the water buffalo farms (used as epidemiological unit in our study), point maps were drawn within the GIS. In addition, for each helminth, clustering of test-positive farms were investigated based on location determined by exact coordinates. Using spatial scan statistic, spatial clusters were found for the flukes <em>F. hepatica</em> and <em>C. daubneyi</em> and the cestode <em>Moniezia</em> spp.; these findings are consistent with the life cycle of these parasites, which have strong environmental determinants. In conclusion, the present study demonstrated that, with the appropriate survey-based data at hand, GIS is a useful tool to study epidemiological patterns of infections in veterinary health, in particular in water buffaloes.

Abstract licence: CC BY-NC

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.