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

A time series analysis of milk productivity in US dairy states

As US dairy cow production evolves, it is important to characterize trends and seasonal patterns to project amounts and fluctuations in milk and milk components by states or regions. Hence, this study aimed to (1) quantify historical trends and seasonal patterns of milk and milk components production associated with calving date by parities and states; (2) classify parities and states with similar trends and seasonal patterns into clusters; and (3) summarize the general pattern for each cluster for further application in simulation models. Our data set contained 9.18 million lactation records from 5.61 million Holstein cows distributed in 17 states during the period January 2006 to December 2016. Each record included a cow's total milk, fat, and protein yield during a lactation. We used time series decomposition to obtain each state's annual trend and seasonal pattern in milk productivity for each parity. Then, we classified states and parities with agglomerative hierarchical clustering into groups according to 2 methods: (1) dynamic time warping on the original time series and (2) Euclidean distance on extracted features of trend and seasonality from the decomposition. Results showed distinguishable trends and seasonality for all states and lactation numbers for all response variables. The clusters and cluster centroid pattern showed a general upward trend for all yields [energy-corrected milk (ECM), milk, fat, and protein] and a steady trend for fat and protein percent for all states except Texas. We also found a larger seasonality amplitude for all yields (ECM, milk, fat, and protein) from higher lactation numbers and a similar amplitude for fat and protein percent across lactation numbers. The results could be used for advising management decisions according to farm productivity goals. Furthermore, the trend and seasonality patterns could be used to adjust the production level in a specific state, year, and season for farm simulations to accurately project milk and milk components production.

Spatio-Temporal Variation in the Prevalence of Major Mastitis Pathogens Isolated From Bovine Milk Samples Between 2008 and 2017 in Ontario, Canada

An understanding of the spatio-temporal distribution of several groups of mastitis pathogens can help to inform programs for the successful control and management of mastitis. However, in the absence of an active surveillance program such information is not readily available. In this retrospective study we analyzed passive surveillance data from a diagnostic laboratory with an aim to describe the spatio-temporal trend of major mastitis pathogens between 2008 and 2017 in Ontario dairy cattle. Data for all milk culture samples submitted to the Animal Health Laboratory (AHL) at the University of Guelph between 2008 and 2017 was accessed. Descriptive analyses were conducted to identify the major pathogens and Chi-square goodness-of-fit tests were used to compare between multiple proportions. Likewise, univariable logistic regression analysis was performed to determine if there was a change in the probability of isolating the major mastitis pathogens depending on geography or time. Seasonality was assessed by calculating the seasonal relative risk (RR). Of a total of 85,979 milk samples examined, more than half of the samples (61.07%) showed no growth and the proportion of samples that showed no growth almost halved during the study period. Of the samples (36.21%, n = 31,133) that showed any growth, the major bacterial pathogens were Staphylococcus aureus (15.60%), Non-aureus Staphylococci (NAS) (5.04%), Corynebacterium spp. (2.96%), and Escherichia coli (2.00%). Of the NAS, the major species reported were Staphylococcus chromogenes (69.02%), Staphylococcus simulans (14.45%), Staphylococcus epidermidis (12.99%), and Staphylococcus hyicus (2.13%). A temporal change in the prevalence of contagious pathogens like S. aureus and Corynebacterium spp. was observed with an increasing odds of 1.06 and 1.62, respectively. Likewise, except for Trueperella pyogenes, the prevalence of all the major environmental mastitis pathogens increased during the study period. The isolation of most of the pathogens peaked in summer, except for S. aureus, T. pyogenes, and Streptococcus dysgalactiae which peaked in spring months. Interestingly, a regional pattern of isolation of some bacterial pathogens within Ontario was also observed. This study showed a marked spatio-temporal change in the prevalence of major mastitis pathogens and suggests that a regional and seasonal approach to mastitis control could be of value.

Comparative Genomic Analysis of Streptococcus dysgalactiae subspecies dysgalactiae Isolated From Bovine Mastitis in China

Streptococcus dysgalactiae subsp. dysgalactiae (SDSD) is one of the most prevalent pathogens causing bovine mastitis worldwide. However, there is a lack of comprehensive information regarding genetic diversity, complete profiles of virulence factors (VFs), and antimicrobial resistance (AMR) genes for SDSD associated with bovine mastitis in China. In this study, a total of 674 milk samples, including samples from 509 clinical and 165 subclinical mastitis cases, were collected from 17 herds in 7 provinces in China from November 2016 to June 2019. All SDSD isolates were included in phylogenetic analysis based on 16S rRNA and multi-locus sequence typing (MLST). In addition, whole genome sequencing was performed on 12 representative SDSD isolates to screen for VFs and AMR genes and to define pan-, core and accessory genomes. The prevalence of SDSD from mastitis milk samples was 7.57% (51/674). According to phylogenetic analysis based on 16S rRNA, 51 SDSD isolates were divided into 4 clusters, whereas based on MLST, 51 SDSD isolates were identified as 11 sequence types, including 6 registered STs and 5 novel STs (ST521, ST523, ST526, ST527, ST529) that belonged to 2 distinct clonal complexes (CCs) and 4 singletons. Based on WGS information, 108 VFs genes in 12 isolates were determined in 11 categories. In addition, 23 AMR genes were identified in 11 categories. Pan-, core and accessory genomes were composed of 2,663, 1,633 and 699 genes, respectively. These results provided a comprehensive profiles of SDSD virulence and resistance genes as well as phylogenetic relationships among mastitis associated SDSD in North China.

Integrating diverse data sources to predict disease risk in dairy cattle-a machine learning approach

Livestock farming is currently undergoing a digital revolution and becoming increasingly data-driven. Yet, such data often reside in disconnected silos making them impossible to leverage their full potential to improve animal well-being. Here, we introduce a precision livestock farming approach, bringing together information streams from a variety of life domains of dairy cattle to study whether including more and diverse data sources improves the quality of predictions for eight diseases and whether using more complex prediction algorithms can, to some extent, compensate for less diverse data. Using three machine learning approaches of varying complexity (from logistic regression to gradient boosted trees) trained on data from 5,828 animals in 165 herds in Austria, we show that the prediction of lameness, acute and chronic mastitis, anestrus, ovarian cysts, metritis, ketosis (hyperketonemia), and periparturient hypocalcemia (milk fever) from routinely available data gives encouraging results. For example, we can predict lameness with high sensitivity and specificity (F1 = 0.74). An analysis of the importance of individual variables to prediction performance shows that disease in dairy cattle is a product of the complex interplay between a multitude of life domains, such as housing, nutrition, or climate, that including more and diverse data sources increases prediction performance, and that the reuse of existing data can create actionable information for preventive interventions. Our findings pave the way toward 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.

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

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.

Prevalence, etiology, and economic impact of clinical mastitis on large dairy farms in China

This study investigated the continuous monthly prevalence of bovine clinical mastitis (CM) and the distribution of causative pathogens among 36,619 CM milk samples from large dairy farms across seven Chinese provinces from 2015 to 2017 using data from routine CM recording systems. Based on treatment period and cost per cow, withdrawal period, daily milk production, and milk value data from each farm in 2017, we calculated the economic impact of CM at the farm level with 2578-9044 lactating cows per farm. Results showed a wide variation in monthly prevalence of CM (0.6 %-18.2 %) among the seven farms over the study period, indicating regional and temporal differences in the occurrence of CM in China. Enterobacteriaceae were the predominant pathogens across all farms from six provinces except Shandong, in which the Streptococcus spp. was the most prevalent. However, the distribution of various Enterobacteriaceae species differed among farms, and Streptococcus species distribution was strongly associated (Pearson's coefficient, 68.4 %) with location. Monthly economic losses associated with CM showed clear variation, ranging from 12,000-76,000 USD/farm/month. Sensitivity analysis showed that economic loss at the farm level was most sensitive to variation in the prevalence of CM, followed by antibiotic treatment period and daily milk production per cow. To our knowledge, this is the longest running study of CM and the first estimation of its economic impacts in China. Our findings highlight the considerable costs associated with mastitis, and indicate that preventive measures and regional and timely treatment of CM are needed.

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

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.