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Fasciola 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 of F. hepatica that could predict farm-level bulk tank milk positivity. The resulting models identified farm-specific key aspects in regard to the presence of F. 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-level F. hepatica positivity. In study region South, pasture access together with farm-level lameness prevalence, farm-level prevalence of hock lesions, herd size, parity, and farm-level milk fat appeared to be important covariates. The stratification of the analysis by study region allows for the extrapolation of the results to similar settings of dairy husbandry. The local, region-specific modelling of F. hepatica presence in this work contributes to the understanding of on-farm aspects of F. hepatica appearance. The applied technique represents a novel approach in this context to model epidemiological data on fasciolosis which allows for the identification of farms at risk and together with additional findings in regard to the epidemiology of fasciolosis, can facilitate risk assessment and deepen our understanding of on-farm drivers of the occurrence of F. hepatica., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Oehm et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Fasciola hepatica is one of the economically most important endoparasites in cattle production. The aim of the present work was to evaluate the relevance of production level on the associations of on-farm presence of F. hepatica with farm-level milk yield, milk fat, and milk protein in Holstein cows, a specialised dairy breed, and in Simmental cows, a dual purpose breed. Furthermore, we investigated whether differential associations were present depending on breed. Data from 560 dairy farms across Germany housing 93,672 cows were analysed. The presence of F. hepatica antibodies was determined via ELISA on bulk tank milk samples. Quantile regression was applied to model the median difference in milk yield, milk fat, and milk protein depending on the interaction of breed and fluke occurrence. Whereas a reduction in milk yield (-1,206 kg, p < 0.001), milk fat (-22.9 kg, p = 0.001), and milk protein (-41.6 kg, p <0.001) was evident on F. hepatica positive German Holstein farms, only milk fat (-33.8 kg, p = 0.01) and milk protein (-22.6 kg, p = 0.03) were affected on F. hepatica positive German Simmental farms. Subsequently, production traits were modelled within each of the two breeds for low, medium, and high producing farms in the presence of F. hepatica antibodies and of confounders. On Holstein farms, the presence of F. hepatica seropositivity was associated with lower production, while on German Simmental farms such an association was less evident. This work demonstrates that production level is relevant when assessing the associations between the exposure to F. hepatica with production characteristics. Moreover, both models indicate a breed dependence. This could point towards a differential F. hepatica resilience of specialised dairy breeds in comparison with dual purpose breeds., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Oehm et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Fasciola hepatica and Ostertagia ostertagi are cattle parasites with worldwide relevance for economic outcome as well as animal health and welfare. The on-farm exposure of cattle to both parasites is a function of host-associated, intrinsic, as well as environmental and farm-specific, extrinsic, factors. Even though knowledge on the biology of both parasites exists, sophisticated and innovative modelling approaches can help to deepen our understanding of key aspects fostering the exposure of dairy cows to these pathogens. In the present study, multiple multinomial logistic regression models were fitted via neural networks to describe the differences among farms where cattle were not exposed to either F. hepatica or O. ostertagi, to one parasite, or to both, respectively. Farm-specific production and management characteristics were used as covariates to portray these differences. This elucidated inherent farm characteristics associated with parasite exposure. In both studied regions, pasture access for cows, farm-level milk yield, and lameness prevalence were identified as relevant factors. In region 'South', adherence to organic farming principles was a further covariate of importance. In region 'North', the prevalence of cows with a low body condition score, herd size, hock lesion prevalence, farm-level somatic cell count, and study year appeared to be of relevance. The present study broadens our understanding of the complex epidemiological scenarios that could predict differential farm-level parasite status. The analyses have revealed the importance of awareness of dissimilarities between farms in regard to the differential exposure to F. hepatica and O. ostertagi. This provides solid evidence that dynamics and relevant factors differ depending on whether or not cows are exposed to F. hepatica, O. ostertagi, or to both., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Fasciolosis caused by the trematode Fasciola hepatica is an important parasitosis in both livestock and humans across the globe. Chronic infections in cattle are associated with considerable economic losses. As a prerequisite for an effective control and prevention of fasciolosis in cattle fine-scale predictive models on farm-level are needed. Since disease transmission will only occur where the mollusc intermediate host is present, the objective of our research was to develop a regression model that allows to predict the local presence or absence of Galba truncatula as principal intermediate host for Fasciola hepatica in Switzerland. By implementing generalized linear mixed models (GLMMs) a total amount of 70 variables were analysed for their potential influence on the likelihood π i of finding Galba truncatula at a certain site. Important site-specific features could be considered by selecting suitable modelling procedures. The statistical software R was used to conduct regression analysis, performing the grplasso and the glmmLasso method. The selection of parameters was based on 10-fold cross validation and the Bayesian Information Criterion (BIC). This yielded a total number of 19 potential predictor variables for the grplasso and 13 variables for the glmmLasso model, which also included random effects. Nine variables appeared to be relevant predictors for the occurrence of Galba truncatula in both models. These included reed/humid area, spring water, water bodies within a 100 m radius, and trees/bushes as powerful positive predictors. High soil depth, temperatures frequently exceeding 30 °C in the year preceding the search for snails and temperatures below 0 °C especially in the second year before were identified to exert an adverse effect on the occurrence of Galba truncatula. Temperatures measured near ground level proved to be more powerful predictors than macroclimatic parameters. Precipitation values seemed to be of minor impact in the given setting. Both regression models may be convenient for a fine-scale prediction of the occurrence of Galba truncatula, and thus provide useful approaches for the development of future spatial transmission models, mapping the risk of fasciolosis in Switzerland on farm-level., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)