Estimating sea lice infestation pressure on salmon farms: Comparing different methods using multivariate state-space models.

Sea lice are ectoparasites of salmonids, and are considered to be one of the main threats to Atlantic salmon farming. Sea lice infestation on a farm is usually initiated by attachment of the free-living copepodid stage derived from the surrounding water, frequently originating from adult lice on the same farm or from neighboring salmonid farms, referred to as internal and external sources, respectively. Various approaches have been proposed to quantify sea lice infestation pressure on farms to improve the management of this pest. Here, we review and compare five of these methods based on sea lice data from 20 farms located near Grand Manan island in the Bay of Fundy, New Brunswick, Canada. Internal and external infestation pressures (IIP and EIP, respectively) were estimated using different approaches, and their effects were modeled either by a unique parameter for all production cycles or by different parameters for each production cycle, using a multivariate state-space model. Predictive variables, such as water temperature and sea lice treatments, were included in the model, and their effects across production cycles were estimated along with those of other model parameters. Results showed that models with only EIP explained the variation in the data better than models with only IIP, and that models with unique IIP and unique EIP for all cycles were generally associated with the best model fit. The simplest, fixed lag method for calculating infestation pressure had the best predictive performance in our models among the methods studied.

Evaluating sire effects on cow fertility: Timed AI and repeat-breeder dairy cows.

In this single herd observational study, there is investigation of the effects of 81 sires (with 11,424 artificial inseminations) on conception rates in 1790 Holstein cows for 5 years. Sires were catagorized based on the published sire conception rate (SCR) into different sire fertility groups (low, average and high fertility sires). The performance of different-sire fertility groups was assessed in timed artificial insemination (TAI) and repeat-breeder (RB) cows. With this aim, two logistic regression models with sire, inseminator, cow, and lactation random effects were applied to data on pregnancies assessed at days 30 and 70 post-insemination. Fixed effects of sire fertility group, sire breed, cow-fertility status, insemination type, postpartum problems, milk yield, temperature humidity index, and year were evaluated. Results from the analysis indicated there was a significant individual sire effect on conception rates, and large heterogeneity in values for this variable among sires. Results indicate that SCR could be assessed to predict low fertility sires reasonably well, and the predicted probabilities for pregnancy per AI (P/AI) at 30 and 70 days post-insemination for high fertility sires were consistent for the most part with values for the SCR. The sire breed did not affect conception rates at days 30 and 70 post-insemination nor its interactions with insemination type (estrous detection AI (EDAI) compared with TAI) and cow-fertility status (RB compared with non-RB). Predicting response probabilities for sires with at least 100 inseminations in each insemination group resulted in greater conception probabilities in cows in which there was EDAI than those in the TAI group.

Using state-space models to predict the abundance of juvenile and adult sea lice on Atlantic salmon.

Sea lice are marine parasites affecting salmon farms, and are considered one of the most costly pests of the salmon aquaculture industry. Infestations of sea lice on farms significantly increase opportunities for the parasite to spread in the surrounding ecosystem, making control of this pest a challenging issue for salmon producers. The complexity of controlling sea lice on salmon farms requires frequent monitoring of the abundance of different sea lice stages over time. Industry-based data sets of counts of lice are amenable to multivariate time-series data analyses. In this study, two sets of multivariate autoregressive state-space models were applied to Chilean sea lice data from six Atlantic salmon production cycles on five isolated farms (at least 20 km seaway distance away from other known active farms), to evaluate the utility of these models for predicting sea lice abundance over time on farms. The models were constructed with different parameter configurations, and the analysis demonstrated large heterogeneity between production cycles for the autoregressive parameter, the effects of chemotherapeutant bath treatments, and the process-error variance. A model allowing for different parameters across production cycles had the best fit and the smallest overall prediction errors. However, pooling information across cycles for the drift and observation error parameters did not substantially affect model performance, thus reducing the number of necessary parameters in the model. Bath treatments had strong but variable effects for reducing sea lice burdens, and these effects were stronger for adult lice than juvenile lice. Our multivariate state-space models were able to handle different sea lice stages and provide predictions for sea lice abundance with reasonable accuracy up to five weeks out.

Maternal and non-maternal factors associated with late embryonic and early fetal losses in dairy cows.

Defining factors associated with embryonic and/or fetal losses will be helpful in overcoming such problem, either by adjusting conditions or applying therapeutic approaches to high-risk cows. The objective of this study was to investigate the association between a number of maternal and non-maternal factors and the risk of late embryonic (LED) and early fetal death (EFD) in dairy herds. Additionally, we investigated the effect of treating pregnant cows either with GnRH on day 26 post-insemination, or GnRH on day 26 plus CIDR insertion between days 26 and 33 post-insemination, on the risk of LED/EFD. From 3826 pregnancies, diagnosed at day 30 post-insemination, 851 cows lost the pregnancy by day 70 post-insemination. A mixed-effects logistic model was constructed to assess the effect of cow breed, calving difficulty, postpartum problems, lactation number, days in milk, insemination number, actual 305-day milk production, temperature humidity index (THI) at insemination, estrus synchronization protocols, and other factors, on the risk of LED/EFD. Our findings indicated that Holstein X Brown Swiss crossbreed cows had a lower risk for LED/EFD than Holstein cows (P < 0.05). Cows that had postpartum problems, were inseminated for the first time, produced more milk, or were inseminated at THI ≥75, recorded higher risks of LED/EFD (P < 0.05). Calving difficulty, lactation number, and synchronization protocols were not found to be associated with LED/EFD. Moreover, treatment of the pregnant cows with GnRH on day 26 post-insemination plus CIDR insertion between days 26 and 33 post-insemination decreased the risk of LED/EFD. In conclusion, cows that had postpartum problems, were inseminated early postpartum, produced higher milk, and/or were inseminated at high THI, were under higher risk of LED/EFD. Treating such cows with GnRH on day 26 plus CIDR insertion between days 26 and 33 may decrease the possibility of the LED/EFD.

Correlated versus uncorrelated frailty Cox models: A comparison of different estimation procedures.

In many studies in medicine, including clinical trials and epidemiological investigations, data are clustered into groups such as health centers or herds in veterinary medicine. Such data are usually analyzed by hierarchical regression models to account for possible variation between groups. When such variation is large, it is of potential interest to explore whether additionally the effect of a within-group predictor varies between groups. In survival analysis, this may be investigated by including two frailty terms at group level in a Cox proportional hazards model. Several estimation methods have been proposed to estimate this type of frailty Cox models. We review four of these methods, apply them to real data from veterinary medicine, and compare them using a simulation study.

Survival analysis of clinical mastitis data using a nested frailty Cox model fit as a mixed-effects Poisson model.

Mastitis is a complex disease affecting dairy cows and is considered to be the most costly disease of dairy herds. The hazard of mastitis is a function of many factors, both managerial and environmental, making its control a difficult issue to milk producers. Observational studies of clinical mastitis (CM) often generate datasets with a number of characteristics which influence the analysis of those data: the outcome of interest may be the time to occurrence of a case of mastitis, predictors may change over time (time-dependent predictors), the effects of factors may change over time (time-dependent effects), there are usually multiple hierarchical levels, and datasets may be very large. Analysis of such data often requires expansion of the data into the counting-process format - leading to larger datasets - thus complicating the analysis and requiring excessive computing time. In this study, a nested frailty Cox model with time-dependent predictors and effects was applied to Canadian Bovine Mastitis Research Network data in which 10,831 lactations of 8035 cows from 69 herds were followed through lactation until the first occurrence of CM. The model was fit to the data as a Poisson model with nested normally distributed random effects at the cow and herd levels. Risk factors associated with the hazard of CM during the lactation were identified, such as parity, calving season, herd somatic cell score, pasture access, fore-stripping, and proportion of treated cases of CM in a herd. The analysis showed that most of the predictors had a strong effect early in lactation and also demonstrated substantial variation in the baseline hazard among cows and between herds. A small simulation study for a setting similar to the real data was conducted to evaluate the Poisson maximum likelihood estimation approach with both Gaussian quadrature method and Laplace approximation. Further, the performance of the two methods was compared with the performance of a widely used estimation approach for frailty Cox models based on the penalized partial likelihood. The simulation study showed good performance for the Poisson maximum likelihood approach with Gaussian quadrature and biased variance component estimates for both the Poisson maximum likelihood with Laplace approximation and penalized partial likelihood approaches.

A cross-classified and multiple membership Cox model applied to calf mortality data.

A cross-classified and multiple membership Cox model was applied to calf mortality data from Western Canada, where 23,409 calves from 174 herds were followed for up to 180 days after calving. The herds were cross-classified by 49 veterinary clinics and 9 ecological regions and in a multiple membership relation to the veterinary clinics, resulting in a 3-level cross-classified and multiple membership data structure. The model was formulated in a mixed-effects Poisson model framework with normally distributed random effects, and was fitted to the data by Bayesian Markov Chain Monte Carlo (MCMC) estimation. Important fixed effects included whether the calf was a twin, calf gender, assistance at calving, cow age, average temperature the first week after calving, the percentage of the herd that had already calved, whether calf shelters were provided, whether cow-calf pairs were moved to a nursery area, and whether any animals were purchased into the herd at or near the time of calving. The analysis demonstrated a greater variation among herds than among both ecological regions and veterinary clinics. Further, a simulation study for a setting similar to the real data gave evidence that the used approach provides valid estimates.