Better targeting treatments against Bovine Respiratory Disease by combining dynamic generalized linear models and mechanistic modelling
Abstract
Bovine Respiratory Disease (BRD) is a major health challenge for young bulls. To minimize economic losses, collective treatments have been widely adopted. Nevertheless, performing collective treatment involves a trade-off between BRD cumulative incidence and severity, and antimicrobial usage (AMU). This raises the question about the optimal timing for treatment. To overcome this challenge, we propose a proof of concept of a decision support tool aimed at helping farmers and veterinarians make informed decisions about the appropriate timing for performing collective treatment for BRD. The proposed framework integrates a mechanistic stochastic simulation engine for modelling the spread of a BRD pathogen (Mannheimia haemolytica), and a hierarchical multivariate binomial dynamic generalized linear model (DGLM). The latter provides early warnings based on the estimated risk of infection. In total, we studied 48 scenarios, using synthetic data, involving two batch sizes (small and large), four farm risk levels of BRD (low, medium, balanced, and high), two allocation systems in batches (sorted by risk level or randomly allocated), and three treatment
intervention types (individual, conventional collective, and collective triggered by the DGLM early warnings). In most scenarios, collective treatments triggered by the DGLM were associated with a reduction of the cumulative incidence and severity of BRD cases. Collective treatments triggered by early warnings typically exhibited either lower or equivalent AMU compared to conventional collective treatments. However, in the Low and Balanced-risk scenarios, the use of DGLM-based collective treatments did not provide an added advantage. Additionally, the DGLM estimates of the risks of infection performed well in the first time steps of the simulation when compared to the true empirical risks. Our findings highlight the potential of the proposed decision support tool in providing valuable guidance for improving animal welfare and AMU. Further validation through real-world data collected from on-farm situations is necessary