Spatial analysis and risk mapping of infection in dairy cattle at the Peruvian central highlands.

This study aimed to develop maps for infection occurrence in dairy cattle in the districts of Matahuasi and Baños in the Peruvian central highlands. For this, a model based on the correlation between environmental variables and the prevalence of infection was constructed. Flukefinder® coprological test were performed in samples from dairy cattle from 8 herds, during both the rainy and wet season. Grazing plots were geo-referenced to obtain information on environmental variables. Monthly temperature, monthly rainfall, elevation, slope, normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference water index (NDWI), distance to rivers, urban areas and roads were obtained by using remote sensor images and ArcGIS®. Multilayer perceptron Artificial Neural Networks modeling were applied to construct a predictive model for the occurrence of fasciolosis, based on the relationship between environmental variables and level of infection. Kappa coefficient (k > 0.6) was used to evaluate concordance between observed and forecasted risk by the model. Coprological results demonstrated an average prevalence from 20% to 100%, in Matahuasi, and between 0 and 87.5%, in Baños. A model with a high level of concordance between predicted and observed infection risk (k = 0.77) was obtained, having as major predicting variables: slope, NDWI, NDVI and EVI. Fasciolosis risk was categorized as low ( < 20%), medium (20% <  < 50%) and high ( ≥ 50%) level. Using ArcGIS 10.4.1, risk maps were developed for each risk level of fasciolosis. Maps of fasciolosis occurrence showed that 87.2% of Matahuasi area presented a high risk for bovine fasciolosis during the dry season, and 76.6% in the wet season. In contrast, 21.9% of Baños area had a high risk of infection during the dry season and 12.1% during the wet season. In conclusion, our model showed areas with high risk for fasciolosis occurrence in both districts during both dry and rainy periods. Slope, NDWI, NDVI and EVI were the major predictors for fasciolosis occurrence.