Effects of exposure to Bovine viral diarrhoea virus 1 on risk of bovine respiratory disease in Australian feedlot cattle

Viruses play a key role in the complex aetiology of bovine respiratory disease (BRD). Bovine viral diarrhoea virus 1 (BVDV-1) is widespread in Australia and has been shown to contribute to BRD occurrence. As part of a prospective longitudinal study on BRD, effects of exposure to BVDV-1 on risk of BRD in Australian feedlot cattle were investigated. A total of 35,160 animals were enrolled at induction (when animals were identified and characteristics recorded), held in feedlot pens with other cattle (cohorts) and monitored for occurrence of BRD over the first 50 days following induction. Biological samples collected from all animals were tested to determine which animals were persistently infected (PI) with BVDV-1. Data obtained from the Australian National Livestock Identification System database were used to determine which groups of animals that were together at the farm of origin and at 28 days prior to induction (and were enrolled in the study) contained a PI animal and hence to identify animals that had probably been exposed to a PI animal prior to induction. Multi-level Bayesian logistic regression models were fitted to estimate the effects of exposure to BVDV-1 on the risk of occurrence of BRD.Although only a total of 85 study animals (0.24%) were identified as being PI with BVDV-1, BVDV-1 was detected on quantitative polymerase chain reaction in 59% of cohorts. The PI animals were at moderately increased risk of BRD (OR 1.9; 95% credible interval 1.0-3.2). Exposure to BVDV-1 in the cohort was also associated with a moderately increased risk of BRD (OR 1.7; 95% credible interval 1.1-2.5) regardless of whether or not a PI animal was identified within the cohort. Additional analyses indicated that a single quantitative real-time PCR test is useful for distinguishing PI animals from transiently infected animals.The results of the study suggest that removal of PI animals and/or vaccination, both before feedlot entry, would reduce the impact of BVDV-1 on BRD risk in cattle in Australian feedlots. Economic assessment of these strategies under Australian conditions is required. © 2016 Elsevier B.V.

Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data

Understanding how aquatic species grow is fundamental in fisheries because stock assessment often relies on growth dependent statistical models. Length-frequency-based methods become important when more applicable data for growth model estimation are either not available or very expensive. In this article, we develop a new framework for growth estimation from length-frequency data using a generalized von Bertalanffy growth model (VBGM) framework that allows for time-dependent covariates to be incorporated. A finite mixture of normal distributions is used to model the length-frequency cohorts of each month with the means constrained to follow a VBGM. The variances of the finite mixture components are constrained to be a function of mean length, reducing the number of parameters and allowing for an estimate of the variance at any length. To optimize the likelihood, we use a minorization–maximization (MM) algorithm with a Nelder–Mead sub-step. This work was motivated by the decline in catches of the blue swimmer crab (BSC) (Portunus armatus) off the east coast of Queensland, Australia. We test the method with a simulation study and then apply it to the BSC fishery data.