Search

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. [ABSTRACT FROM AUTHOR]

Swine production in the United States is characterized by dynamic farm contacts through animal movements; such movements shape the risk of disease occurrence on farms. Pig movements have been linked to the spread of a virulent porcine reproductive and respiratory syndrome virus (PRRSV), RFLP type 1‐7‐4, herein denoted as phylogenetic sub‐lineage 1A [L1A]. This study aimed to quantify the contribution of pig movements to the risk of L1A occurrence on farms in the United States. Farms were defined as L1A‐positive in a given 6‐month period if at least one L1A sequence was recovered from the farm. Temporal network autocorrelation modelling was performed using data on animal movements and 1,761 PRRSV ORF5 sequences linked to 494 farms from a dense pig production area in the United States between 2014 and 2017. A farm's current and past exposure to L1A and other PRRSV variants was assessed through its primary and secondary contacts in the animal movement network. Primary and secondary contacts with an L1A‐positive farm increased the likelihood of L1A occurrence on a farm by 19% (p =.04) and 23% (p =.03), respectively. While the risk posed by primary contacts with PRRS‐positive farms is unsurprising, the observation that secondary contacts also increase the likelihood of infection is novel. Risk of L1A occurrence on a farm also increased by 3.0% (p =.01) for every additional outgoing shipment, possibly due to biosecurity breaches during loading and transporting pigs from the farm. Finally, use of vaccines or field virus inoculation on sow farms one year prior reduced the risk of L1A occurrence in downstream farms by 36% (p =.04), suggesting that control measures that reduce viral circulation and enhance immunological protection in sow farms have a carry‐over effect on L1A occurrence in downstream farms. Therefore, coordinated disease management interventions between farms connected via animal movements may be more effective than individual farm‐based interventions. [ABSTRACT FROM AUTHOR]

Viral sequence data coupled with phylodynamic models have become instrumental in investigating the outbreaks of human and animal diseases, and the incorporation of the hypothesized drivers of pathogen spread can enhance the interpretation from phylodynamic inference. Integrating animal movement data with phylodynamics allows us to quantify the extent to which the spatial diffusion of a pathogen is influenced by animal movements and contrast the relative importance of different types of movements in shaping pathogen distribution. We combine animal movement, spatial, and environmental data in a Bayesian phylodynamic framework to explain the spatial diffusion and evolutionary trends of a rapidly spreading sub-lineage (denoted L1A) of porcine reproductive and respiratory syndrome virus (PRRSV) Type 2 from 2014 to 2017. PRRSV is the most important endemic pathogen affecting pigs in the USA, and this particular virulent sub-lineage emerged in 2014 and continues to be the dominant lineage in the US swine industry to date. Data included 984 open reading frame 5 (ORF5) PRRSV L1A sequences obtained from two production systems in a swine-dense production region (∼85,000 mi2) in the USA between 2014 and 2017. The study area was divided into sectors for which model covariates were summarized, and animal movement data between each sector were summarized by age class (wean: 3–4 weeks; feeder: 8–25 weeks; breeding: ≥21 weeks). We implemented a discrete-space phylogeographic generalized linear model using Bayesian evolutionary analysis by sampling trees (BEAST) to infer factors associated with variability in between-sector diffusion rates of PRRSV L1A. We found that between-sector spread was enhanced by the movement of feeder pigs, spatial adjacency of sectors, and farm density in the destination sector. The PRRSV L1A strain was introduced in the study area in early 2013, and genetic diversity and effective population size peaked in 2015 before fluctuating seasonally (peaking during the summer months). Our study underscores the importance of animal movements and shows, for the first time, that the movement of feeder pigs (8–25 weeks old) shaped the spatial patterns of PRRSV spread much more strongly than the movements of other age classes of pigs. The inclusion of movement data into phylodynamic models as done in this analysis may enhance our ability to identify crucial pathways of disease spread that can be targeted to mitigate the spatial spread of infectious human and animal pathogens. [ABSTRACT FROM AUTHOR]

Porcine reproductive and respiratory syndrome (PRRS) is an economically devastating disease of swine in many parts of the world. Porcine reproductive and respiratory syndrome virus (PRRSV) type 1 is endemic in Europe, and prevalence of the subtypes differ spatially. In this study, we investigated a severe PRRS outbreak reported in 30 farms located in eastern Russia that belong to a large swine production company in the region that was also experiencing a pseudorabies outbreak in the system. Data included 28 ORF5 sequences from samples across 18 of the 25 infected sites, reverse transcriptase real-time polymerase chain reaction (RT-qPCR) results from diagnostic testing, reports of clinical signs, and animal movement records. We observed that the outbreak was due to two distinct variants of wildtype PRRSV type 1 subtype 1 with an average genetic distance of 15%. Results suggest that the wildtype PRRSV variants were introduced into the region around 2019, before affecting this production system (i.e., sow farms, nurseries, and finisher farms). Clinical signs did not differ between the variants, but they did differ by stage of pig production. Biosecurity lapses, including movement of animals from infected farms contributed to disease spread. [ABSTRACT FROM AUTHOR]

As a consequence of multi-site pig production practiced in North America, frequent and widespread animal movements create extensive networks of interaction between farms. Social network analysis (SNA) has been used to understand disease transmission risks within these complex and dynamic production ecosystems and is particularly relevant for designing risk-based surveillance and control strategies targeting highly connected farms. However, inferences from SNA and the effectiveness of targeted strategies may be influenced by temporal changes in network structure. Since farm movements represent a temporally dynamic network, it is also unclear how many months of data are required to gain an accurate picture of an individual farm's connectivity pattern and the overall network structure. The extent to which shipments between two specific farms are repeated (i.e., "loyalty" of farm contacts) can influence the rate at which the structure of a network changes over time, which may influence disease dynamics. In this study, we aimed to describe temporal stability and loyalty patterns of pig movement networks in the U.S. swine industry. We analyzed a total of 282,807 animal movements among 2724 farms belonging to two production systems between 2014 and 2017. Loyalty trends were largely driven by contacts between sow farms and nurseries and between nurseries and finisher farms; mean loyalty (percent of contacts that were repeated at least once within a 52-week interval) of farm contacts was 51–60 % for farm contacts involving weaned pigs, and 12–22% for contacts involving feeder pigs. A cyclic pattern was observed for both weaned and feeder pig movements, with episodes of increased loyalty observed at intervals of 8 and 17–20 weeks, respectively. Network stability was achieved when six months of data were aggregated, and only small shifts in node-level and global network metrics were observed when adding more data. This stability is relevant for designing targeted surveillance programs for disease management, given that movements summarized over too short a period may lead to stochastic swings in network metrics. A temporal resolution of six months would be reliable for the identification of potential super-spreaders in a network for targeted intervention and disease control. The temporal stability observed in these networks suggests that identifying highly connected farms in retrospective network data (up to 24 months) is reliable for future planning, albeit with reduced effectiveness. [ABSTRACT FROM AUTHOR]

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. 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. Here, we construct movement and spatial proximity networks; 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. Our results indicated that 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. [ABSTRACT FROM AUTHOR]