Researchers VanderWaal, Paploski, Makau and Corzo at the University of Minnesota provide new insights into how PRRSv spreads between farms and the importance of data sharing in this research article published in Preventive Veterinary Medicine.

Highlights

• The study combined three years of PRRSv genetic data with network analysis to look at the dynamics of between-farm spread of PRRSv.
• Data from a subset of the MSHMP farms was used and included farm location, animal movements between farms, and any PRRSv sequence recovered those farms.
• The researchers identified between-farm infection chains and elucidated types of contact that were most associated with PRRSv transmission.
• Results showed that animal movements, not local area spread, play a dominant role in shaping transmission pathways.
• Local area spread ( within a 5 km area) also contributed to the PRRSv transmission pathway, though to a much lesser extent than animal movements.

Implications for COVID-19?

Molecular geneticists and epidemiologists perform similar work for the human population, especially now during the COVID-19 pandemic.  Follow this link to learn how researchers trace the routes the virus has traveled across the world in an attempt to find out how quickly and easily SARS-CoV-2 spreads using globally shared data.

Introduction

Analyses of livestock movement networks has become key to understanding an industry’s vulnerability to infectious disease spread and for identifying farms that play disproportionate roles in pathogen dissemination. In addition to animal movements, many pathogens can spread between farms via mechanisms mediated by spatial proximity. Heterogeneities in contact patterns based on spatial proximity are less commonly considered in network studies, and studies that jointly consider spatial connectivity and animal movement are rare. The objective of this study was to determine the extent to which movement versus spatial proximity networks determine the distribution of an economically important endemic virus, porcine reproductive and respiratory syndrome virus (PRRSv), within a swine-dense region of the U.S.

Materials & Methods

PRRSv can be classified into numerous phylogenetic lineages. Such data can be used to better resolve between-farm infection chains and elucidate types of contact most associated with transmission. Movement and spatial proximity networks were constructed; farms within the networks were classified as cases if a given PRRSv lineage had been recovered at least once in a year for each of three years analyzed. We evaluated six lineages and sub-lineages across three years, and evaluated the epidemiological relevance of each network by applying network k-tests to statistically evaluate whether the pattern of case occurrence within the network was consistent with transmission via network linkages.

Results & Discussion

Animal movements, not local area spread, play a dominant role in shaping transmission pathways, though there were differences amongst lineages. The median number of case farms inter-linked via animal movements was approximately 4.1x higher than random expectations (range: 1.7–13.7; p < 0.05, network k-test), whereas this measure was only 2.7x higher than random expectations for farms linked via spatial proximity (range: 1.3–5.4; p < 0.05, network k-test). For spatial proximity networks, contact based on proximities of <5 km appeared to have greater epidemiological relevance than longer distances, likely related to diminishing probabilities of local area spread at greater distances. However, the greater overall levels of connectivity of the spatial network compared to the movement network highlights the vulnerability of pig populations to widespread transmission via this route. By combining genetic data with network analysis, this research advances our understanding of dynamics of between-farm spread of PRRSv, helps establish the relative importance of transmission via animal movements versus local area spread, and highlights the potential for targeted control strategies based upon heterogeneities in network connectivity.

To read about this work in more detail, refer to the full manuscript here.