Research done as part of the Horizon Europe project 'Enhanced and cost-effective biosecurity in livestock production', BIOSECURE. Abstract Effective prevention of pathogen transmission through biosecurity practices is vital for maintaining pig health and productivity. This study presents HogDSim, a novel within-farm model for farrow-to-finish pig farms. The model incorporates epidemiological scenarios that account for farm structure and pathogen spread via both animal and farmer movement. Farmer mobility is tracked using Bluetooth®-enabled personal beacons and detection points, allowing high-resolution monitoring of human-mediated transmission routes. We demonstrate the model using swine influenza A virus (swIAV) as a case study in a single farrow-to-finish farm. Transmission parameters were calibrated via Approximate Bayesian Computation Sequential Monte Carlo across scenarios with varying data availability, where synthetic infection data were generated from literature-based swIAV parameters. Additionally, we offer a concise demonstration of the model’s application in evaluating biosecurity measures. HogDSim is a modular framework for simulating within-farm disease dynamics. It integrates epidemiological processes, animal and farmer movements, an auxiliary transmission route, and modules for detection and ABC-SMC calibration. Using swIAV as a case study, we show that calibration from fattening-area observations can recover main transmission pathways, although auxiliary-route parameters remain difficult to identify under sparse surveillance. While demonstrated on swIAV, the framework is adaptable to diverse pathogens, biosecurity strategies, and farm configurations, supporting reproducible scenario comparison.
Tubay et al. (Tue,) studied this question.