Combining dynamic generalized linear models and mechanistic modelling to optimize treatment strategies against bovine...

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 treatments involves a trade-off between BRD cumulative incidence and severity, and antimicrobial usage (AMU). Therefore, 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), which provides early warnings based on infection risk estimates. Using synthetic data, we studied 48 scenarios, involving two batch sizes (small and large), four farm risk levels for developing BRD (low, medium, balanced, and high), two batch allocation systems (sorted by risk level or randomly allocated), and three treatment interventions (individual, conventional collective, and DGLM-based collective). In high- and medium-risk scenarios, collective treatments triggered by the DGLM were associated with a reduction in BRD cumulative incidence and disease severity, especially in large populations. Compared with conventional treatments, DGLM-based collective treatments typically result in either lower or equivalent AMU, with the largest reductions being observed in medium-, balanced-, and high-risk scenarios. Additionally, the DGLM estimates of infection risk aligned well with the empirical risk estimates during the first time steps of the simulation. These 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.