Multinomial logistic regression based on neural networks reveals inherent differences among dairy farms depending on the differential exposure to Fasciola hepatica and Ostertagia ostertagi

[Display omitted] •Analysis of large, cross-regional data demonstrates the power of machine learning.•Dairy farms have distinct dissimilarities based on varying parasite exposure.•Relevant factors can remarkably differ across epidemiological settings.•Local and farm level conditions are paramount in...

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Veröffentlicht in:International journal for parasitology 2023-10, Vol.53 (11-12), p.687-697
Hauptverfasser: Oehm, Andreas W., Leinmueller, Markus, Zablotski, Yury, Campe, Amely, Hoedemaker, Martina, Springer, Andrea, Jordan, Daniela, Strube, Christina, Knubben-Schweizer, Gabriela
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container_end_page 697
container_issue 11-12
container_start_page 687
container_title International journal for parasitology
container_volume 53
creator Oehm, Andreas W.
Leinmueller, Markus
Zablotski, Yury
Campe, Amely
Hoedemaker, Martina
Springer, Andrea
Jordan, Daniela
Strube, Christina
Knubben-Schweizer, Gabriela
description [Display omitted] •Analysis of large, cross-regional data demonstrates the power of machine learning.•Dairy farms have distinct dissimilarities based on varying parasite exposure.•Relevant factors can remarkably differ across epidemiological settings.•Local and farm level conditions are paramount in dairy cow poly-parasitism. 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.
doi_str_mv 10.1016/j.ijpara.2023.05.006
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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. 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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. 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subjects Cluster analysis
Dairy cattle helminths
Epidemiology
Modelling
Multi-parasitism
title Multinomial logistic regression based on neural networks reveals inherent differences among dairy farms depending on the differential exposure to Fasciola hepatica and Ostertagia ostertagi
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