A hidden Markov model to predict early mastitis from test-day somatic cell scores

Bivariate threshold models for genetic evaluation of susceptibility to and ability to recover from mastitis in Danish Holstein cows

Mastitis in dairy cows is an unavoidable problem and genetic variation in recovery from mastitis, in addition to susceptibility, is therefore of interest. Genetic parameters for susceptibility to and recovery from mastitis were estimated for Danish Holstein-Friesian cows using data from automatic milking systems equipped with online somatic cell count measuring units. The somatic cell count measurements were converted to elevated mastitis risk, a continuous variable [on a (0-1) scale] indicating the risk of mastitis. Risk values >0.6 were assumed to indicate that a cow had mastitis. For each cow and lactation, the sequence of health states (mastitic or healthy) was converted to a weekly transition: 0 if the cow stayed within the same state and 1 if the cow changed state. The result was 2 series of transitions: one for healthy to diseased (HD, to model mastitis susceptibility) and the other for diseased to healthy (DH, to model recovery ability). The 2 series of transitions were analyzed with bivariate threshold models, including several systematic effects and a function of time. The model included effects of herd, parity, herd-test-week, permanent environment (to account for the repetitive nature of transition records from a cow) plus two time-varying effects (lactation stage and time within episode). In early lactation, there was an increased risk of getting mastitis but the risk remained stable afterwards. Mean recovery rate was 45% per lactation. Heritabilities were 0.07 [posterior mean of standard deviations (PSD) = 0.03] for HD and 0.08 (PSD = 0.03) for DH. The genetic correlation between HD and DH has a posterior mean of -0.83 (PSD = 0.13). Although susceptibility and recovery from mastitis are strongly negatively correlated, recovery can be considered as a new trait for selection.

Factors affecting the probability of bacteriological cure of bovine mastitis

The aim of this study was to review factors affecting the probability of cure of bovine mastitis and thereby establish criteria for deciding whether to treat or cull individual animals. A further objective was to avoid redundant treatment with antibiotics so as to reduce the risk of pathogen resistance and enhance economic benefit. In evaluating success of therapy, bacteriological cure is the standard type of cure and is defined as elimination of mastitis-causing pathogens from the mammary gland. Administration of antibiotics is considered reasonable only when there is a prospect of bacteriological cure. In addition to age of the affected cow, the history of mastitis, number of infected quarters and somatic cell count affect the probability of bacteriological cure. Identifying and characterising chronic mastitis, which causes enormous production losses, are especially important to prevent unnecessary treatment and to decide whether or not to cull. To our knowledge, this is the first work providing a complete list of factors that have been confirmed in scientific literature to influence the probability of cure. This review should support farmers and veterinarians in deciding between culling and administering appropriate therapy to an affected animal.

Evaluating somatic cell scores with a Bayesian Gaussian linear state-space model

Because accurate characterization of health state is important for managing dairy herds, we propose to validate the use of a linear state-space model (LSSM) for evaluating monthly somatic cell scores (SCSs). To do so, we retrieved SCS from a dairy database and collected reports on clinical mastitis collected in 20 farms, during the period from January 2008 to December 2011 in the Walloon region of Belgium. The dependent variable was the SCS, and the independent variables were the number of days from calving, year of calving and parity. The LSSM also incorporated an error-free underlying variable that described the trend across time as a function of previous clinical and subclinical status. We computed the mean sum of squared differences between observed SCS and median values of the posterior SCS distribution and constructed the receiver operating characteristic (ROC) curve for SCS thresholds going from 0 to 6. Our results show SCS estimates are close to observed SCS and area under the ROC curve is higher than 90%. We discuss the meaning of the parameters in light of our current knowledge of the disease and propose methods to incorporate, in LSSM, this knowledge often expressed in the form of ordinary differential equations.