Land altitude, slope, and coverage as risk factors for Porcine Reproductive and Respiratory Syndrome (PRRS) outbreaks in the United States

Andréia Gonçalves Arruda, Carles Vilalta, Andres M Perez, Robert B Morrison

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


Porcine reproductive and respiratory syndrome (PRRS) is, arguably, the most impactful disease on the North American swine industry. The Swine Health Monitoring Project (SHMP) is a national volunteer initiative aimed at monitoring incidence and, ultimately, supporting swine disease control, including PRRS. Data collected through the SHMP currently represents approximately 42% of the sow population of the United States. The objective of the study here was to investigate the association between geographical factors (including land elevation, and land coverage) and PRRS incidence as recorded in the SHMP. Weekly PRRS status data from sites participating in the SHMP from 2009 to 2016 (n = 706) was assessed. Number of PRRS outbreaks, years of participation in the SHMP, and site location were collected from the SHMP database. Environmental features hypothesized to influence PRRS risk included land coverage (cultivated areas, shrubs and trees), land altitude (in meters above sea level) and land slope (in degrees compared to surrounding areas). Other risk factors considered included region, production system to which the site belonged, herd size, and swine density in the area in which the site was located. Land-related variables and pig density were captured in raster format from a number of sources and extracted to points (farm locations). A mixed-effects Poisson regression model was built; and dependence among sites that belonged to a given production system was accounted for using a random effect at the system level. The annual mean and median number of outbreaks per farm was 1.38 (SD: 1.6), and 1 (IQR: 2.0), respectively. The maximum annual number of outbreaks per farm was 9, and approximately 40% of the farms did not report any outbreak. Results from the final multivariable model suggested that increments of swine density and herd size increased the risk for PRRS outbreaks (P < 0.01). Even though altitude (meters above sea level) was not significant in the final model, farms located in terrains with a slope of 9% or higher had lower rates of PRRS outbreaks compared to farms located in terrains with slopes lower than 2% (P < 0.01). Finally, being located in an area of shrubs/ herbaceous cover and trees lowered the incidence rate of PRRS outbreaks compared to being located in cultivated/managed areas (P < 0.05). In conclusion, highly inclined terrains were associated with fewer PRRS outbreaks in US sow farms, as was the presence of shrubs and trees when compared to cultivated/ managed areas. Influence of terrain characteristics on spread of airborne diseases, such as PRRS, may help to predicting disease risk, and effective planning of measures intended to mitigate and prevent risk of infection.

Original languageEnglish (US)
Article numbere0172638
JournalPloS one
Issue number4
StatePublished - Apr 2017

Bibliographical note

Funding Information:
Funding for the Swine Health Monitoring Project and this study was provided by the Swine Health Information Center, the National Pork Board, and the MnDrive program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors would like to acknowledge the Swine Health Monitoring Project participants for sharing information.

Publisher Copyright:
© 2017 Arruda et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Dive into the research topics of 'Land altitude, slope, and coverage as risk factors for Porcine Reproductive and Respiratory Syndrome (PRRS) outbreaks in the United States'. Together they form a unique fingerprint.

Cite this