Adapting an Atmospheric Dispersion Model to Assess the Risk of Windborne Transmission of Porcine Reproductive and Respiratory Syndrome Virus between Swine Farms

Kaushi S.T. Kanankege, Kerryne Graham, Cesar A. Corzo, Kimberly VanderWaal, Andres M. Perez, Peter A. Durr

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Modeling the windborne transmission of aerosolized pathogens is challenging. We adapted an atmospheric dispersion model (ADM) to simulate the windborne dispersion of porcine reproductive and respiratory syndrome virus (PRRSv) between swine farms. This work focuses on determining ADM applicable parameter values for PRRSv through a literature and expert opinion-based approach. The parameters included epidemiological features of PRRSv, characteristics of the aerosolized particles, and survival of aerosolized virus in relation to key meteorological features. A case study was undertaken to perform a sensitivity analysis on key parameters. Farms experiencing ongoing PRRSv outbreaks were assigned as particle emitting sources. The wind data from the North American Mesoscale Forecast System was used to simulate dispersion. The risk was estimated semi-quantitatively based on the median daily deposition of particles and the distance to the closest emitting farm. Among the parameters tested, the ADM was most sensitive to the number of particles emitted, followed by the model runtime, and the release height was the least sensitive. Farms within 25 km from an emitting farm were at the highest risk; with 53.66% being within 10 km. An ADM-based risk estimation of windborne transmission of PRRSv may inform optimum time intervals for air sampling, plan preventive measures, and aid in ruling out the windborne dispersion in outbreak investigations.

Original languageEnglish (US)
Article number1658
JournalViruses
Volume14
Issue number8
DOIs
StatePublished - Aug 2022

Bibliographical note

Funding Information:
TAPPAS: This research was undertaken with the assistance of resources from the National Computational Infrastructure (NCI), which is supported by the Australian Government. Acknowledgement: Albert Rovira of the University of Minnesota and Scott Dee of the Pipestone Veterinary Services for sharing their expertise. Swine Health Information Center (SHIC) for the funding for Morrison Swine Health Monitoring Project (MSHMP) of the University of Minnesota. The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and/or READY website ( https://www.ready.noaa.gov , accessed on 20 January 2020) used in this publication.

Funding Information:
This research was funded by United States Department of Agriculture (USDA)—Agriculture and Food Research Initiative (AFRI) 2018: grant number (2019-67030-29569).

Publisher Copyright:
© 2022 by the authors.

Keywords

  • HYSPLIT
  • Lagrangian models
  • TAPPAS
  • aerial dispersion
  • airborne
  • infectious disease modeling
  • pig diseases
  • spatial epidemiology

PubMed: MeSH publication types

  • Journal Article
  • Research Support, U.S. Gov't, Non-P.H.S.

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