[Geographical Information Systems and remote sensing technologies in parasitological epidemiology]

Parasites have natural habitats in the same way as a species: they are found in focal areas where the spatial distribution of the parasite, host, vector and required environmental conditions coincide. The spatial distribution of parasites is, therefore, a function of the interaction between abiotic and biotic environmental factors. The boundaries of distributions are not strictly fixed in space and time and may fluctuate with climate and other components of the environment or anthropical factors. Geographic Information Systems (GIS) and remote sensing (RS) technologies are being used increasingly to study the spatial and temporal patterns of disease. GIS can be used to complement conventional ecological monitoring and modelling techniques, and provide means to portray complex relationships in the ecology of disease. In addition, the use of GIS and RS to identify environmental features allows determination of risk factors and delimitation of areas at risk, permitting more rational allocation of resources for cost-effective control. Since 1996, GIS have been used in our territorial cross-sectional and longitudinal parasitological surveys in order to experiment new applications to plan sampling protocols and to display quickly, clearly, and analytically the spatial and/or temporal distribution of parasitological data. The use of GIS allowed us to draw the following types of descriptive parasitological maps: distribution maps, distribution maps with proportioned peaks, choroplethic maps with proportioned peaks, point distribution maps and point distribution maps with proportioned peaks. In a recent study, GIS and RS technologies have been used also to identify environmental features that influence the distribution of paramphistomosis in sheep from the southern Italian Apennines and to develop a preliminary risk assessment model. A GIS was constructed using RS and landscape feature data together with paramphistome positive survey records from 197 georeferenced ovine farms with animals pasturing in an area of the southern Italian Apennines. The GIS for the study area was constructed utilizing the following environmental variables: Normalized Difference Vegetation Index (NDVI), land cover, elevation, slope, aspect, and total length of rivers. In addition, data regarding the presence of watercourses smaller than rivers, namely, streams, springs and brooks were recorded in the field. All these variables were then calculated for "buffer zones" consisting of the areas included in a circle of 3 Km diameter centred on 197 farms. The environmental data obtained were analyzed by univariate and multivariate statistical analyses using the paramphistome farm coprological status (positive/negative) as the dependent variable. A multivariate stepwise discriminant analysis model was developed that included moors and heathland, sclerophyllus and coniferous forest vegetation, autumn-winter NDVI and presence of streams, springs and brooks on pasture. The variables entered in the model are consistent with the environmental requirements of paramphistomes and their snail intermediate host. In particular, the land cover types entered in the model in this area are indicators of marginal uncultivable and sloping zones where typically there is the presence of water (permanently or temporarily). In addition, since NDVI can be used as an indicator of regional thermal-moisture regime, the distribution of farms positive for paramphistomosis corresponding to relatively high values of winter NDVI indicated the presence of adequate moisture and temperatures favourable to the rumen fluke and the snails. In conclusion, GIS and RS are useful to define the habitats of parasites, especially for those with strong environmental determinants, and to produce forecasting maps requested for the planning and the monitoring of control strategies on small and large scale.