GIS modeling for canine dirofilariosis risk assessment in central Italy
AbstractA survey was conducted in an area of central Italy in order to study the prevalence of Dirofilaria immitis and D. repens in dogs. Blood samples were collected from 283 dogs and examined using a modified Knott’s technique. In addition, in order to detect D. immitis occult infection, 203 serum samples were also analysed for D. immitis antigen detection. The results were analyzed in order to evaluate the behavioural and attitudinal risk factors. A geographical information system (GIS) for the study area was constructed, utilizing the following data layers: administrative boundaries, elevation, temperature, rainfall and humidity. Microfilariae were detected in 32 of the 283 dogs surveyed, constituting a total Dirofilaria prevalence of 11.3%. In particular, 20 dogs (7.1%) were positive for D. immitis and 12 dogs (4.2%) for D. repens microfilariae. One case of D. immitis occult infection was also detected. Choroplethic municipal maps were drawn within the GIS in order to display the distribution of each Dirofilaria species in the study area. Statistical analysis showed a significant association between Dirofilaria infection and animal attitude (hunting/truffle dogs showed a higher prevalence compared to guard/pet dogs). A higher prevalence was also recorded in 2 to 5-years old dogs. Furthermore a GIS-based modelling of climatic data, collected from 5 meteorological stations in the study area, was performed to estimate the yearly number of D. immitis generations in the mosquito vector. The results of the model as depicted by GIS analysis was highly concordant with the territorial distribution of positive dogs and showed that D. immitis spreading is markedly influenced by season. The potential transmission period in the study area was found to be confined to summer months with a peak in July and August, as expected for a temperate region where summer season is the most favourable period for the parasite.
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Copyright (c) 2008 Michele Mortarino, Vincenzo Musella, Valeria Costa, Claudio Genchi, Giuseppe Cringoli, Laura Rinaldi
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