A hybrid within-farm model combines detailed agent dynamics with compartmental efficiency.
Effective prevention of pathogen transmission through biosecurity practices is vital for maintaining pig health and productivity.
A study by Jerrold M Tubay of Universiteit Utrecht and his team from the same university and Royal GD, 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.
The researchers 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, they offer a concise demonstration of the model’s application in evaluating biosecurity measures.
Results
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, the researchers 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.
Read the full article here.
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