David F. Kelton

Human noroviruses in swine and cattle.

Detection of GII.4 norovirus sequences in animal fecal samples and retail meats demonstrates that noroviruses may be transmitted zoonotically.

A review of network analysis terminology and its application to foot-and-mouth disease modelling and policy development.

Livestock movements are important in spreading infectious diseases and many countries have developed regulations that require farmers to report livestock movements to authorities. This has led to the availability of large amounts of data for analysis and inclusion in computer simulation models developed to support policy formulation. Social network analysis has become increasingly popular to study and characterize the networks resulting from the movement of livestock from farm-to-farm and through other types of livestock operations. Network analysis is a powerful tool that allows one to study the relationships created among these operations, providing information on the role that they play in acquiring and spreading infectious diseases, information that is not readily available from more traditional livestock movement studies. Recent advances in the study of real-world complex networks are now being applied to veterinary epidemiology and infectious disease modelling and control. A review of the principles of network analysis and of the relevance of various complex network theories to infectious disease modelling and control is presented in this paper.

Invited review: Changes in the dairy industry affecting dairy cattle health and welfare

The dairy industry in the developed world has undergone profound changes over recent decades. In this paper, we present an overview of some of the most important recent changes in the dairy industry that affect health and welfare of dairy cows, as well as the science associated with these changes. Additionally, knowledge gaps are identified where research is needed to guide the dairy industry through changes that are occurring now or that we expect will occur in the future. The number of farms has decreased considerably, whereas herd size has increased. As a result, an increasing number of dairy farms depend on hired (nonfamily) labor. Regular professional communication and establishment of farm-specific protocols are essential to minimize human errors and ensure consistency of practices. Average milk production per cow has increased, partly because of improvements in nutrition and management but also because of genetic selection for milk production. Adoption of new technologies (e.g., automated calf feeders, cow activity monitors, and automated milking systems) is accelerating. However, utilization of the data and action lists that these systems generate for health and welfare of livestock is still largely unrealized, and more training of dairy farmers, their employees, and their advisors is necessary. Concurrently, to remain competitive and to preserve their social license to operate, farmers are increasingly required to adopt increased standards for food safety and biosecurity, become less reliant on the use of antimicrobials and hormones, and provide assurances regarding animal welfare. Partly because of increasing herd size but also in response to animal welfare regulations in some countries, the proportion of dairy herds housed in tiestalls has decreased considerably. Although in some countries access to pasture is regulated, in countries that traditionally practiced seasonal grazing, fewer farmers let their dairy cows graze in the summer. The proportion of organic dairy farms has increased globally and, given the pressure to decrease the use of antimicrobials and hormones, conventional farms may be able to learn from well-managed organic farms. The possibilities of using milk for disease diagnostics and monitoring are considerable, and dairy herd improvement associations will continue to expand the number of tests offered to diagnose diseases and pregnancy. Genetic and genomic selection for increased resistance to disease offers substantial potential but requires collection of additional phenotypic data. There is every expectation that changes in the dairy industry will be further accentuated and additional novel technologies and different management practices will be adopted in the future.

A principal component regression based genome wide analysis approach reveals the presence of a novel QTL on BTA7 for MAP resistance in holstein cattle.

Bovine Johnes disease (JD), caused by Mycobacterium avium spp. paratuberculosis (MAP), causes significant losses to the dairy and beef cattle industries. Effective vaccination or therapeutic strategies against this disease are currently unavailable and infected animals either get culled or die due to clinical disease. An alternative strategy to manage the disease is to selectively breed animals with enhanced resistance to MAP infection. Therefore, the objective of this study was to identify genetic loci putatively associated with MAP infection in a resource population consisting of Holstein cattle using a genome-wide association approach. The BovineSNP50 BeadChip, containing 54,001 single nucleotide polymorphisms (SNPs), was used to genotype 232 animals with known MAP infection status. Since, traditional case-control analytical techniques are based on single-marker analysis and do not account for the existence of linkage disequilibrium (LD) between markers, we used a novel principal component regression approach, where each SNP was fit in a logistic regression model, along with principal components of other SNPs on the same chromosome showing association with the trait, as covariates. Such an approach allowed us to account for the LD that exists between multiple markers showing an association on the same chromosome. Our analysis revealed the presence of at least 12 genomic regions on BTA1, 5, 6, 7, 10, 11 and 14 that were associated with the MAP infection status of our resource population. A brief description of these genomic regions, and a discussion of the analysis used in this study, have been presented.

The potential for zoonotic transmission of Giardia duodenalis and Cryptosporidium spp. from beef and dairy cattle in Ontario, Canada.

The objective of this study was to compare the occurrence and the genotypes and species of Giardia duodenalis and Cryptosporidium spp. in beef and dairy cattle from farms in the Regional Municipality of Waterloo, Ontario, in an effort to determine the potential for zoonotic transmission from these animals. Pooled manure samples were collected from 45 dairy cattle farms and 30 beef cattle farms. The presence of Giardia cysts and Cryptosporidium oocysts was determined by immunofluorescence microscopy, while nested-PCR and DNA sequencing were used to determine genotypes and species. The overall farm prevalence was very high for both Giardia and Cryptosporidium, and was similar for dairy cattle farms (96 and 64%, respectively) and beef cattle farms (97 and 63%, respectively). However, on dairy cattle farms, G. duodenalis and Cryptosporidium spp. were detected in 44% and 6% of total pooled pen manure samples, respectively, with the occurrence of both parasites being generally higher in calves than in older animals. Most Giardia isolates were identified as either the host-adapted genotype G. duodenalis Assemblage E or the zoonotic Assemblage B. Cryptosporidium parvum and Cryptosporidium andersoni were the most frequently identified species in dairy cattle, while the non-zoonotic species Cryptosporidium ryanae and Cryptosporidium bovis were also found. On beef cattle farms, 72% and 27% of the total pooled pen manure samples were positive for Giardia and Cryptosporidium, respectively, with no obvious correlation with age. All Giardia isolates in beef cattle were identified as G. duodenalis Assemblage E, while all Cryptosporidium isolates were identified by sequence analysis as C. andersoni, although microscopic analyses, and subsequent restriction fragment length polymorphism analyses, indicated that other Cryptosporidium species were also present. The results of this study indicate that although Giardia and Cryptosporidium were identified in a higher overall percentage of the pooled beef cattle manure samples than in dairy cattle, firmly established zoonotic genotypes and species were much more common in dairy cattle than in beef cattle in this region. Dairy cattle, and especially dairy calves, may, therefore, pose a greater risk of infection to humans than beef cattle. However, these results may also provide evidence of potential zooanthroponotic transmission (human to animal).

Comparing Network Analysis Measures to Determine Potential Epidemic Size of Highly Contagious Exotic Diseases in Fragmented Monthly Networks of Dairy Cattle Movements in Ontario, Canada

Adult milking cow movements occurring in monthly periods in 2004-2006 were analysed to compare three network analysis measures to determine the lower and upper bounds of potential maximal epidemic size in an unrestrained epidemic: the out-degree, the infection chain or output domain of a farm, and the size of the strong and weak components. The directed networks generated by the movements of adult milking cows were highly fragmented. When all the farms that were not involved in shipments were included in the analysis, the risk of infection transmission through movements of adult cows was very low. To determine the size of an epidemic when an infected farm shipped cows in such a fragmented network, farm out-degree and infection chain provided similar and more reasonable estimates of potential maximal epidemic size than the size of the strong and weak components. Component analysis always provided estimates that were two to three times larger than the out-degree of infection chain approaches. For example, the upper bound was estimated to be 12-13 farms using out-degree and 16-17 farms using the infection chain, the components approach showed a range of 39-51 potentially exposed farms. Strong components provided an inflated measure of the lower bound of potential maximal epidemic size at first diagnosis because the time sequence of shipments was not considered. Weak components provided an inflated measure of the upper bound because both the time sequence and directionality of shipments between farms were ignored. Farm degree and infection chain measures should now be tested to determine their usefulness for estimating maximum epidemic size in large connected networks.

Management of the dry cow in control of peripartum disease and mastitis

The dry period has great implications on overall health and productivity in the subsequent lactation. Many anatomic, physiologic, and immunolgic changes are occurring for both the cow and the mammary gland during this time. These changes need to be understood and taken into consideration when assessing and implementing health management programs that involve this crucial time period. Specifically, nutritional and immunologic requirements of the individual cow need to be considered. The occurrence of many peripartum diseases is significantly influenced through nutritional and metabolic parameters that can be strongly influenced, controlled, and monitored in the dry period. From an udder-health perspective, the goal of the dry period can be met by recommending administration of DCT to all quarters of all cows at the end of lactation. As research continues to explore and define shortcomings of this approach and as scrutiny of the prophylactic use of antibiotics increases, however, novel approaches to preventing and eliminating IMI may become more readily available. These approaches offer new methods to improve upon and redefine what should be realistic goals of the dry period and afford an opportunity for continued improvement of udder health in todays dairy herds.

Health recording in Canadian Holsteins: Data and genetic parameters

The objective of this study was to investigate if health data recorded by Canadian dairy producers can be used for genetic selection. Eight diseases are recorded by producers on a voluntary basis: mastitis, displaced abomasum, ketosis, milk fever, retained placenta, metritis, cystic ovaries, and lameness. Between 40 to 60% of all herds had to be excluded by editing procedures for each trait, assuming unreliable health recording. All analyses were carried out for first-lactation Holstein cows. The majority of disease cases occurred in the first month of lactation. Mean disease frequencies were 12.6, 3.7, 4.5, 4.6, 10.8, 8.2, and 9.2% for mastitis, displaced abomasum, ketosis, retained placenta, metritis, cystic ovaries, and lameness, respectively. Milk fever was very rare in first-lactation cows with a frequency of only 0.20%, and was, therefore, not considered in the analyses. Univariate and bivariate linear animal models were fitted. Heritabilities for mastitis, displaced abomasum, ketosis, retained placenta, metritis, cystic ovaries, and lameness were 0.02, 0.06, 0.03, 0.03, 0.02, 0.03, and 0.01, respectively. Genetic correlations between diseases were mostly positive. The strongest genetic correlations were found between displaced abomasum and ketosis (0.64) and between retained placenta and metritis (0.62). The remaining genetic correlations ranged from -0.22 (between metritis and lameness) to 0.49 (between mastitis and lameness). In agreement with the genetic correlations, the largest phenotypic correlations were found between displaced abomasum and ketosis (0.27) and retained placenta and metritis (0.14). All other phenotypic correlations were low and close to zero (0.00 to 0.06). Pearson correlations between breeding values for health traits and other routinely evaluated traits were computed, which revealed noticeable favorable relationships to direct herd life and fertility. In addition, a moderate favorable association was found between mastitis and somatic cell score. Mastitis is the most promising trait to be included in routine genetic evaluation, because it is the most recorded disease and has a high frequency and positive genetic correlations to all other health traits. Although, about 40% of all Canadian dairy producers participate in the health-recording system, a large proportion of the data are lost after data validation. Thus, dairy producers should be encouraged to keep accurate and complete health data.

From explanation to prediction: a model for recurrent bovine tuberculosis in Irish cattle herds.

There is a good understanding of factors associated with bovine tuberculosis (BTB) risk in Irish herds. As yet, however, this knowledge has not been incorporated into predictive models with the potential for improved, risk-based surveillance. The goal of the study was to enhance the national herd scoring system for BTB risk, thus leading to improved identification of cattle herds at high risk of recurrent BTB episodes. A retrospective cohort study was conducted to develop a statistical model predictive of recurrent bovine tuberculosis episodes in cattle herds in the Republic of Ireland. Herd-level disease history data for the previous 12 years, the previous 3 years, the previous episode, and the current-episode were used in survival analyses to determine the aspects of disease history that were predictive of a recurrent breakdown within 3 years of a cleared BTB episode. Relative to herds with 0-1 standard reactors in the current BTB episode, hazard ratios increased to 1.3 and 1.6 for herds with 2-5 and >5 standard reactors, respectively. Compared to herds with <30 animals, hazard ratios increased from 1.8 to 2.5 and then to 3.1 for herds with 30-79, 80-173, and >174 animals respectively. Relative to herds with <4 herd-level tests in the previous 3 years, herds with 4-5 and >5 tests had 1.1 and 1.4 times greater hazard of a BTB breakdown. Herds that did not have a BTB episode in the 5 years prior to their 2001 episode were 0.8 times less likely to breakdown in the next 3 years than herds that did. Herds breaking down in the spring or summer were 0.8 times less likely to suffer a recurrent breakdown than herds breaking down in autumn or winter (this was likely due to seasonality in testing regimes). The presence of a confirmed BTB lesion was not predictive of increased risk of recurrent BTB. Despite the availability of detailed disease history, the predictive ability of the model was poor. One explanation for this was that herds suffering a recurrence of BTB on their first test after clearing a BTB episode were different from herds that broke down later in the period at risk. Future research might need to include additional variables to identify which subsets of herd BTB episodes, if any, have identifiable features that are predictive of recurrent breakdowns.

Alternative somatic cell count traits to improve mastitis resistance in Canadian Holsteins.

The objective of this study was to investigate whether alternative somatic cell count (SCC) traits are suitable as mastitis indicators in Canadian Holsteins. Mastitis data recorded by producers were available from the national dairy cattle health system in Canada. Mastitis was defined as a binary variable based on whether or not the cow had at least one mastitis case in the period from calving to 305 d after calving. The analyzed alternative SCC traits included mean somatic cell score (SCS) from different time periods, maximum SCS, standard deviation of SCS, excessive test-day SCC, and a peak pattern of test-day records with suspicion of mastitis. Data of 53,626 first-lactation Holstein cows from 1,666 herds across Canada were analyzed using linear animal models. A heritability of 0.02 was obtained for mastitis. For both mean SCS in early and late lactation, a heritability of 0.11 was estimated. Heritabilities of various patterns of SCC ranged from 0.01 to 0.07. Estimated genetic correlations were 0.69 and 0.68 between mastitis and mean SCS in early and late lactation, respectively. Higher genetic correlations were found between mastitis and the different SCC patterns (0.82 to 0.91). Sires with high breeding values for mastitis resistance had consistently higher percentage of healthy daughters than sires with low breeding values for mastitis resistance. Breeding values for mean SCS in early lactation, standard deviation of SCS, and an excessive test-day SCC pattern (at least one SCC test-day above 500,000) were the best predictors of the breeding value for mastitis resistance and explained in total 41% of the variation in relative breeding values for mastitis resistance. The results demonstrated that patterns of SCC provide additional information for genetic evaluations of mastitis resistance that cannot be explained by mean SCS alone.