Unveiling invisible farm-to-farm PRRSV-2 transmission links and routes through transmission tree and network analysis.

The United States (U.S.) swine industry has struggled to control porcine reproductive and respiratory syndrome (PRRS) for decades, yet the causative virus, PRRSV-2, continues to circulate and rapidly diverges into new variants. In the swine industry, the farm is typically the epidemiological unit for monitoring, prevention, and control; breaking transmission among farms is a critical step in containing disease spread. Despite this, our understanding of farm transmission still is inadequate, precluding the development of tailored control strategies. Therefore, our objective was to infer farm-to-farm transmission links, estimate farm-level transmissibility as defined by reproduction numbers ( R ), and identify associated risk factors for transmission using PRRSV-2 open reading frame 5 (ORF5) gene sequences, animal movement records, and other data from farms in a swine-dense region of the U.S. from 2014 to 2017. Timed phylogenetic and transmission tree analyses were performed on three sets of sequences ( n  = 206) from 144 farms that represented the three largest genetic variants of the virus in the study area. The length of inferred pig-to-pig infection chains that corresponded to pairs of farms connected via direct animal movement was used as a threshold value for identifying other feasible transmission links between farms; these links were then transformed into farm-to-farm transmission networks and calculated farm-level R -values. The median farm-level R was one (IQR = 1-2), whereas the R value of 28% of farms was more than one. Exponential random graph models were then used to evaluate the influence of farm attributes and/or farm relationships on the occurrence of farm-to-farm transmission links. These models showed that, even though most transmission events cannot be directly explained by animal movement, movement was strongly associated with transmission. This study demonstrates how integrative techniques may improve disease traceability in a data-rich era by providing a clearer picture of regional disease transmission.