Modelling the transmission dynamics of bovine digital dermatitis in New Zealand pastoral dairy production systems.

Bovine digital dermatitis (DD) is an important infectious cause of cattle lameness worldwide that has become increasingly prevalent in New Zealand pastoral dairy herds. In this study, a simplified DD scoring system after considering both M and Iowa DD scoring systems was applied to explore the transmission dynamics of DD in a typical spring-calving pastoral New Zealand dairy herd. The modified model only included three compartments: normal skin, early stage lesions and advanced lesions. Lesions regressing after treatment were excluded as DD lesions are rarely treated in New Zealand. Furthermore, sub-classes within each lesion class were not defined due to the lack of variability in DD lesion presentations within New Zealand. The model was validated based on longitudinal field data from three dairy herds in the Waikato region during one lactation season (2017-18). The model suggested that in infected dairy herds, although DD prevalence will tend to increase year-on-year it is likely to remain relatively low (< 18%) even after 10 years of within-herd transmission. It is likely that the low transmission rate during the late lactation (model assumption) results in more cases resolving than developing during this period and therefore results in the low prevalence of infectious cattle at the start of each subsequent lactation. Cattle with advanced lesions had a stronger influence on the establishment and maintenance of DD than cattle with early stage lesions highlighting the importance of targeting these animals for intervention. On-going monitoring of DD is highly recommended to assess the long-term progression of the disease in affected dairy herds.

Keeping continuous diagnostic data continuous: Application of Bayesian latent class models in veterinary research.

Bayesian finite mixture models, frequently referred to as Bayesian latent class models have become increasingly common for diagnostic test data in the absence of a gold standard test. Most Bayesian analyses in the veterinary literature have dealt with a dichotomised diagnostic outcome. The use of Bayesian finite mixture models for continuous test outcomes, such as sample to positive (S/P) ratios produced by an ELISA, is much less common, despite continuous models taking advantage of all of the information captured in the test outcome. This paper revisits the idea of the Bayesian finite mixture model and provides a practical guide for researchers who would like to use this approach for modelling continuous diagnostic outcomes as it preserves all information from the observed data. Synthetic datasets and a dataset from literature were analysed to illustrate that a mixture model with continuous diagnostic outcomes can be used to estimate true prevalence and to evaluate test sensitivity and specificity. In addition, directly modelling the continuous test outcomes rather than dichotomising them, means that optimal cut-offs can be defined based on the test purpose rather than being determined before testing. Moreover, as animals with higher scores are more likely to be infected, using continuous data allows test interpretation to be made at the individual animal level. In contrast, dichotomization treats all animals above a cut-off as having the same infection risk. This study demonstrates that dichotomisation is not a 'must' when using Bayesian latent class analysis for diagnostic test data, and suggests that latent class analysis using continuous test outcomes should be favoured when evaluating veterinary diagnostic tests producing continuous outcomes.

Design-Based Approach for Analysing Survey Data in Veterinary Research.

Sample surveys are an essential approach used in veterinary research and investigation. A sample obtained from a well-designed sampling process along with robust data analysis can provide valuable insight into the attributes of the target population. Two approaches, design-based or model-based, can be used as inferential frameworks for analysing survey data. Compared to the model-based approach, the design-based approach is usually more straightforward and directly makes inferences about the finite target population (such as the dairy cows in a herd or dogs in a region) rather than an infinite superpopulation. In this paper, the concept of probability sampling and the design-based approach is briefly reviewed, followed by a discussion of the estimations and their justifications in the context of several different elementary sampling methods, including simple random sampling, stratified random sampling, and one-stage cluster sampling. Finally, a concrete example of a complex survey design (involving multistage sampling and stratification) is demonstrated, illustrating how finding unbiased estimators and their corresponding variance formulas for a complex survey builds on the techniques used in elementary sampling methods.