Modeling the Seasonal Variation of Windborne Transmission of Porcine Reproductive and Respiratory Syndrome Virus between Swine Farms.

Abstract

Modeling the windborne transmission of aerosolized pathogens is challenging. We adapted an atmospheric dispersion model named the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to simulate the windborne dispersion of porcine reproductive and respiratory syndrome virus (PRRSv) between swine farms and incorporated the findings into an outbreak investigation. The risk was estimated semi-quantitatively based on the cumulative daily deposition of windborne particles and the distance to the closest emitting farm with an ongoing outbreak. Five years of data (2014:2018) were used to study the seasonal differences of the deposition thresholds of the airborne particles containing PRRSv and to evaluate the model in relation to risk prediction and barn air filtration. When the 14-day cumulative deposition was considered, in winter, above-threshold particle depositions would reach up to 30 km from emitting farms with 84% of them being within 10 km. Long-distance pathogen transmission was highest in winter and fall, lower in spring, and least in summer. The model successfully replicated the observed seasonality of PRRSv, where fall and winter posed a higher risk for outbreaks. Reaching the humidity and temperature thresholds tolerated by the virus in spring and summer reduced the survival and infectivity of aerosols beyond 10-20 km. Within the data limitations of voluntary participation, when wind was assumed to be the sole route of PRRSv transmission, the predictive performance of the model was fair with >0.64 AUC. Barn air filtration was associated with fewer outbreaks, particularly when exposed to high levels of viral particles. This study confirms the usefulness of the HYSPLIT model as a tool when determining seasonal effects and distances and informs the near real-time risk of windborne PRRSv transmission that can be useful in future outbreak investigations and for implementing timely control measures.