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

Daniel Alexis Zárate-Rendón; David Godoy Padilla; Samuel Pizarro Carcausto; Alberto Del Águila; Eric Wetzel; Javier Ñaupari Vásquez

doi:10.1016/j.parepi.2023.e00329

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

Parasite Epidemiol Control. 2023 Nov 23:23:e00329. doi: 10.1016/j.parepi.2023.e00329. eCollection 2023 Nov.

Authors

Daniel Alexis Zárate-Rendón¹, David Godoy Padilla¹, Samuel Pizarro Carcausto², Alberto Del Águila³, Eric Wetzel³, Javier Ñaupari Vásquez²

Affiliations

¹ Laboratorio de Parasitología, Departamento Académico de Nutrición, Facultad de Zootecnia, Universidad Nacional Agraria La Molina, Lima, Peru.
² Laboratorio de Ecología y Utilización de Pastizales, Departamento Académico de Producción Animal, Facultad de Zootecnia, Universidad Nacional Agraria La Molina, Lima, Peru.
³ Global Health Initiative, Wabash College, 301 W Wabash Ave, Crawfordsville, IN 47933, USA.

Abstract

This study aimed to develop maps for Fasciola hepatica 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 (p < 20%), medium (20% < p < 50%) and high (p ≥ 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.

Keywords: Cattle; Central highlands; Fasciola hepatica; GIS; Peru; Remote sensors; Risk mapping.