Random forest classification as a tool in epidemiological modelling: Identification of farm-specific characteristics relevant for the occurrence of < i > Fasciola hepatica < /i > on German dairy farms

by Andreas W. Oehm, Yury Zablotski, Amely Campe, Martina Hoedemaker, Christina Strube, Andrea Springer, Daniela Jordan, Gabriela Knubben-SchweizerFasciola hepatica is an internal parasite of both human and veterinary relevance. In order to control fasciolosis, a multitude of attempts to predict the risk of infection such as risk maps or forecasting models have been developed. These attempts mainly focused on the influence of geo-climatic and meteorological features. Predicting bovine fasciolosis on farm level taking into account farm-specific settings yet remains challenging. In the present study, a new methodology for this purpose, a data-driven machine learning approach using a random forest classification algorithm was applied to a cross-sectional data set of farm characteristics, management regimes, and farmer aspects within two structurally different dairying regions in Germany in order to identify factors relevant for the occurrence ofF.hepatica that could predict farm-level bulk tank milk positivity. The resulting models identified farm-specific key aspects in regard to the presence ofF.hepatica. In study region North, farm-level production parameters (farm-level milk yield, farm-level milk fat, farm-level milk protein), leg hygiene, body condition (prevalence of overconditioned and underconditioned cows, respectively) and pasture access were identified as features relevant in regard to farm-levelF.hepatica positivity. In study region South, pasture access together wit...
Source: PLoS One - Category: Biomedical Science Authors: Source Type: research