Comparison of the epidemiological behavior of mastitis pathogens by applying time-series analysis in results of milk samples submitted for microbiological examination

Fernández, Gonzalo; Barreal, María Luisa; Pombo, M. B.; Ginzo‐Villamayor, María José; González–Manteiga, Wenceslao; Prieto, Alberto; Lago, N.; González-Palencia, J.

doi:10.1007/s11259-013-9570-1

Cited by 10 publications

(7 citation statements)

References 43 publications

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“…Contagious pathogens generally spread from cow to cow with the infected udder being the primary source of infection whereas environmental pathogens, which are found in the environment where the cow resides, spread directly to the udder from the environment [ 2 ]. Staphylococcus aureus , a major mastitis pathogen is commonly considered a contagious pathogen, although recently it has been recognized that its epidemiological behaviour is not clear cut, with strains demonstrating contagious and/or environmental transmission patterns [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Extensive Genomic Diversity among Bovine-Adapted Staphylococcus aureus: Evidence for a Genomic Rearrangement within CC97

et al. 2015

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Staphylococcus aureus is an important pathogen associated with both human and veterinary disease and is a common cause of bovine mastitis. Genomic heterogeneity exists between S. aureus strains and has been implicated in the adaptation of specific strains to colonise particular mammalian hosts. Knowledge of the factors required for host specificity and virulence is important for understanding the pathogenesis and management of S. aureus mastitis. In this study, a panel of mastitis-associated S. aureus isolates (n = 126) was tested for resistance to antibiotics commonly used to treat mastitis. Over half of the isolates (52%) demonstrated resistance to penicillin and ampicillin but all were susceptible to the other antibiotics tested. S. aureus isolates were further examined for their clonal diversity by Multi-Locus Sequence Typing (MLST). In total, 18 different sequence types (STs) were identified and eBURST analysis demonstrated that the majority of isolates grouped into clonal complexes CC97, CC151 or sequence type (ST) 136. Analysis of the role of recombination events in determining S. aureus population structure determined that ST diversification through nucleotide substitutions were more likely to be due to recombination compared to point mutation, with regions of the genome possibly acting as recombination hotspots. DNA microarray analysis revealed a large number of differences amongst S. aureus STs in their variable genome content, including genes associated with capsule and biofilm formation and adhesion factors. Finally, evidence for a genomic arrangement was observed within isolates from CC97 with the ST71-like subgroup showing evidence of an IS431 insertion element having replaced approximately 30 kb of DNA including the ica operon and histidine biosynthesis genes, resulting in histidine auxotrophy. This genomic rearrangement may be responsible for the diversification of ST71 into an emerging bovine adapted subgroup.

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Section: Introductionmentioning

confidence: 99%

Extensive Genomic Diversity among Bovine-Adapted Staphylococcus aureus: Evidence for a Genomic Rearrangement within CC97

et al. 2015

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“…It is worth noting that chronic mastitis is the only condition for which the predictor trained on the reduced data set yielded a higher sensitivity than the one trained on the full data set. Two of the four existing studies on the application of ML algorithms to mastitis phenomenology 30, 31 do not predict disease incidence and a direct comparison with our approach is not possible. The other two approaches outperform our approach with sensitivities of >93% 33 and 97.7% 32 respectively.…”

Section: Resultsmentioning

confidence: 99%

“…The literature on the prediction of diseases is frequently based on black-box sensor systems in which prediction algorithms are used that are of commercial interest and not publicly known or evaluated 26 or the reported sensitivity and specificity of prediction algorithms varies widely 9 . Diseases for which successful applications of ML approaches have been reported include lameness [27][28][29] , mastitis [30][31][32][33] , metabolic status, i.e. ketosis (hyperketonemia) and periparturient hypocalcemia (milk fever) [34][35][36][37][38] , infectious diseases 39,40 and oestrus detection 41 .…”

Section: Introductionmentioning

confidence: 99%

Integrating diverse data sources to predict disease risk in dairy cattle

Lasser

Matzhold

Egger-Danner³

et al. 2021

Preprint

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Livestock farming is currently undergoing a digital revolution and becoming increasingly data-driven. Yet, such data often reside in disconnected silos making it impossible to leverage their full potential to improve animal well-being. Here, we introduce a precision medicine approach, bringing together information streams from a variety of life domains of dairy cattle to predict eight common and economically important diseases. Dairy cows are part of a highly industrialised environment. The animals and their surroundings are closely monitored and environmental, behavioural and physiological observations are readily accessible yet seldomly integrated. We use random forest classifiers trained on data from 5,828 animals in 166 herds in Austria to predict occurrences of lameness, acute and chronic mastitis, anoestrus, ovarian cysts, metritis, ketosis (hyperketonemia) and periparturient hypocalcemia (milk fever). To assess the importance of specific cattle life domains and individual features for these predictions, we use multivariate logistic regression and feature permutation approaches. We show that disease in dairy cattle is a product of the complex interplay between a multitude of life domains such as housing, nutrition or climate, and identify a range of features that were previously not associated with increased disease risk. For example, we can predict anoestrus with high sensitivity and specificity (F1=0.72) and find that housing, feed and husbandry variables such as barn design and time on pasture are most predictive of this disease. We also find previously unknown associations of features with disease risk, for example humid conditions, which significantly decrease the odds for ketosis. Our findings pave the way towards data-driven point-of-care interventions and demonstrate the added value of integrating all available data in the dairy industry to improve animal well-being and reduce disease risk.

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“…Detecting diseases, such as mastitis, early would favor both economics and the cow’s welfare. Autoregressive integrated moving average models and CA were able to detect seasonal trends of mastitis pathogens in quarter milk samples, which are regularly assessed for microbial examination [ 52 ]. These classification models further detected that mastitis pathogens can be classified into both contagious and environmental categories, whereas previously, it has been reported that they could only be either one or the other [ 52 ].…”

Section: Resultsmentioning

confidence: 99%

Review: Application and Prospective Discussion of Machine Learning for the Management of Dairy Farms

Cockburn

2020

Animals

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Dairy farmers use herd management systems, behavioral sensors, feeding lists, breeding schedules, and health records to document herd characteristics. Consequently, large amounts of dairy data are becoming available. However, a lack of data integration makes it difficult for farmers to analyze the data on their dairy farm, which indicates that these data are currently not being used to their full potential. Hence, multiple issues in dairy farming such as low longevity, poor performance, and health issues remain. We aimed to evaluate whether machine learning (ML) methods can solve some of these existing issues in dairy farming. This review summarizes peer-reviewed ML papers published in the dairy sector between 2015 and 2020. Ultimately, 97 papers from the subdomains of management, physiology, reproduction, behavior analysis, and feeding were considered in this review. The results confirm that ML algorithms have become common tools in most areas of dairy research, particularly to predict data. Despite the quantity of research available, most tested algorithms have not performed sufficiently for a reliable implementation in practice. This may be due to poor training data. The availability of data resources from multiple farms covering longer periods would be useful to improve prediction accuracies. In conclusion, ML is a promising tool in dairy research, which could be used to develop and improve decision support for farmers. As the cow is a multifactorial system, ML algorithms could analyze integrated data sources that describe and ultimately allow managing cows according to all relevant influencing factors. However, both the integration of multiple data sources and the obtainability of public data currently remain challenging.

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Comparison of the epidemiological behavior of mastitis pathogens by applying time-series analysis in results of milk samples submitted for microbiological examination

Cited by 10 publications

References 43 publications

Extensive Genomic Diversity among Bovine-Adapted Staphylococcus aureus: Evidence for a Genomic Rearrangement within CC97

Extensive Genomic Diversity among Bovine-Adapted Staphylococcus aureus: Evidence for a Genomic Rearrangement within CC97

Integrating diverse data sources to predict disease risk in dairy cattle

Review: Application and Prospective Discussion of Machine Learning for the Management of Dairy Farms

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