Combining early hyperthermia detection with metaphylaxis for reducing antibiotics usage in newly received beef bulls at fattening operations: a simulation-based approach

Abstract

International audienceBovine Respiratory Disease (BRD) dramatically affects fattened young beef bull pens. How metaphylaxis and early detection help balance disease duration and antibiotics usage remains unclear. Our goal was to determine efficient control strategies, assessed on disease duration, antibiotics doses, and true positives, for various infection forces accounting for BRD pathogen diversity. A stochastic mechanistic individual-based model combined infectious processes, detection methods, and treatment protocols in a realistic simulated small-size pen. To enable veterinary experts to assess and revise model assumptions, a new artificial intelligence framework, EMULSION, was used to describe model features in an explicit and intelligible form. Parameters were calibrated from observed data. Overpassing on-farm reference scenario using boluses required to very early detect the first case while using longer hyperthermia for subsequent detections. Metaphylaxis was efficient only for high pathogen transmission. Besides concrete recommendations to farmers, EMULSION models could easily address other farming systems, treatments, and diseases