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Spatial Analysis of Infections by Toxoplasma gondii and Neospora caninum (Protozoa: Apicomplexa) in Small Ruminants in Northern Italy.

Alessia Gazzonis | Luca Villa | MariaTeresa Manfredi | Sergio Zanzani
Animals : an open access journal from MDPI | 2019

The objectives of this study were: (i) To investigate possible geographical or environmental factors influencing the infections by Toxoplasma gondii and Neospora caninum in sheep and goats in northern Italy; (ii) to identify areas at risk of infection to set up preventive measures. Forty-three sheep and goat farms were included. Their locations were plotted and associated with T. gondii and N. caninum seroprevalence, then the distribution of farms' prevalence was evaluated by spatial analysis. Significant clusters for both low and high prevalence were obtained, and a generalized linear model with ordinal logistic regression was implemented to verify if spatial clustering could be due to climate factors (temperature, rainfall, and their interaction). Clusters of high (80.0%) and low prevalence (28.12%) resulted for T. gondii seroprevalence in sheep farms. No significant clusters resulted for goat farms. Clusters of high (38.68%) and low prevalence (21.23%) resulted for N. caninum seroprevalence in sheep farms. One high-prevalence cluster (15.62%) resulted for goat farms. For goats, spatial analysis and analysis on climatic data showed the absence of environmental significant risk factors associated with T. gondii or N. caninum infection. On the contrary, for sheep, annual temperature, rainfall, and their association affected the risk of T. gondii and N. caninum infection. Particularly, high temperatures and abundant rainfalls were related to T. gondii seroprevalence, while low temperatures and scarce rainfalls were related to N. caninum seroprevalence.

Pubmed ID: 31689940

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WorldClim (tool)

RRID:SCR_010244

A set of global climate layers (climate grids) with a spatial resolution of about 1 square kilometer. The data can be used for mapping and spatial modeling in a GIS or with other computer programs. If you are not familiar with such programs, you can try DIVA-GIS or the R raster package.

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