HogDSim - A within-farm modeling framework for infectious diseases of swine

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.