Development and validation of a farm- and province-level swine flow simulation model using discrete events and...

Infectious disease events can cause disruptions in service-based and agricultural industries. The list of possible events is long and varies from the incursion or emergence of a reportable animal pathogen to the recently documented interruptions caused by the COVID-19 pandemic. There is a need to develop models that can determine the impact of pathogens and mitigation measures on populations that are not directly affected by the pathogen in the case of a reportable disease, particularly when the health and welfare of these populations could be affected due to resulting disruptions in trade and supply chains. The primary objective of this study was to develop a discrete-event simulation (DES) model of swine production, including pork processing, for scenarios without major disruptions, which could be scaled from the level of an individual farm to the entire province of Ontario, Canada. The secondary objective was to validate the developed simulation against observed farm- and province-level statistics. A weekly discrete-event simulation consisting of 3 connected areas (a sow farm, a pig farm, and abattoirs) was developed using AnyLogic modelling software. Using Mann-Whitney tests, model outputs representative of the standard industry statistics were compared to data from 6 individual farms separately, as well as to provincial data from Ontario. A scalable discrete-event simulation of the swine production system for typical scenarios was accomplished. The model outputs were consistent with individual farm and industry statistics. As such, the model can be used to simulate swine production at distinct levels and could be further modified to represent swine marketing in other provinces or internationally.