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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., (© 2021 Wiley-VCH GmbH.)

Porcine reproductive and respiratory syndrome virus genotype 2 (PRRSV-2) genetic diversity in the U.S. was assessed using a database comprising 10 years' worth of sequence data obtained from swine production systems routine monitoring and outbreak investigations. A total of 26,831 ORF5 PRRSV-2 sequences from 34 production systems were included in this analysis. Within group mean genetic distance (i.e. mean proportion of nucleotide differences within ORF5) per year according to herd type was calculated for all PRRSV-2 sequences. The percent nucleotide difference between each sequence and the ORF5 sequences from four commercially available PRRSV-2 vaccines (Ingelvac PRRS MLV, Ingelvac PRRS ATP, Fostera PRRS, and Prevacent PRRS) within the same lineage over time was used to classify sequences in wild-type or vaccine-like. The mean ORF5 genetic distance fluctuated from 0.09 to 0.13, being generally smaller in years in which there was a relative higher frequency of dominant lineage. Vaccine-like sequences comprised about one fourth of sequences obtained through routine monitoring of PRRS. We found that lineage 5 sequences were mostly Ingelvac PRRS MLV-like. Lineage 8 sequences up to 2011 were 62.9% Ingelvac PRRS ATP-like while the remaining were wild-type viruses. From 2012 onwards, 51.9% of lineage 8 sequences were Ingelvac PRRS ATP-like, 45.0% were Fostera PRRS-like, and only 3.2% were wild-type. For lineage 1 sequences, 0.1% and 1.7% of the sequences were Prevacent PRRS-like in 2009-2018 and 2019, respectively. These results suggest that repeated introductions of vaccine-like viruses through use of modified live vaccines might decrease within-lineage viral diversity as vaccine-like strains become more prevalent. Overall, this compilation of private data from routine monitoring provides valuable information on PRRSV viral diversity., Competing Interests: The authors have declared that no competing interests exist. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Porcine epidemic diarrhea virus (PEDv) was introduced to the U.S. in 2013 and is now considered to be endemic. Like many endemic diseases, it is challenging for producers to estimate and respond to spatial and temporal variation in risk. Utilizing a regional spatio-temporal dataset containing weekly PEDv infection status for ∼15 % of the U.S. sow herd, we present a machine learning platform developed to forecast the probability of PEDv infection in sow farms in the U.S. Participating stakeholders (swine production companies) in a swine-dense region of the U.S. shared weekly information on a) PEDv status of farms and b) animal movements for the past week and scheduled movements for the upcoming week. Environmental (average temperature, humidity, among others) and land use characteristics (hog density, proportion of area with different land uses) in a 5 km radius around each farm were summarized. Using the Extreme Gradient Boosting (XGBoost) machine learning model with Synthetic Minority Over-sampling Technique (SMOTE), we developed a near real-time tool that generates weekly PEDv predictions (pertaining to two-weeks in advance) to farms of participating stakeholders. Based on retrospective data collected between 2014 and 2017, the sensitivity, specificity, positive and negative predictive values of our model were 19.9, 99.9, 70.5 and 99.4 %, respectively. Overall accuracy was 99.3 %, although this metric is heavily biased by imbalance in the data (less than 0.7 % of farms had an outbreak each week). This platform has been used to deliver weekly real-time forecasts since December 2019. The forecast platform has a built-in feature to re-train the predictive model in order to remain as relevant as possible to current epidemiological situations, or to expand to a different disease. These dynamic forecasts, which account for recent animal movements, present disease distribution, and environmental factors, will promote data-informed and targeted disease management and prevention within the U.S. swine industry., (Copyright © 2021 Elsevier B.V. All rights reserved.)

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., (Copyright © 2020. Published by Elsevier B.V.)

Understanding between-farm movement patterns is an essential component in developing effective surveillance and control programs in livestock populations. Quantitative knowledge on movement patterns is particularly important for the commercial swine industry, in which large numbers of pigs are frequently moved between farms. Here, we described the annual movement patterns between swine farms in three production systems of the United States and identified farms that may be targeted to increase the efficacy of infectious disease control strategies. Research results revealed a high amount of variability in movement patterns across production systems, indicating that quantities captured from one production system and applied to another may lead to invalid estimations of disease spread. Furthermore, we showed that targeting farms based on their mean infection potential, a metric that captured the temporal sequence of movements, substantially reduced the potential for transmission of an infectious pathogen in the contact network and performed consistently well across production systems. Specifically, we found that by targeting farms based on their mean infection potential, we could reduce the potential spread of an infectious pathogen by 80% when removing approximately 25% of farms in each of the production systems. Whereas other metrics, such as degree, required 26-35% of farms to be removed in two of the production systems to reach the same outcome; this outcome was not achievable in one of the production systems. Our results demonstrate the importance of fine-scale temporal movement data and the need for in-depth understanding of the contact structure in developing more efficient disease surveillance and response strategies in swine production systems., (Copyright © 2019 Elsevier B.V. All rights reserved.)

The spread of pathogens in swine populations is in part determined by movements of animals between farms. However, understanding additional characteristics that predict disease outbreaks and uncovering landscape factors related to between-farm spread are crucial steps toward risk mitigation. This study integrates animal movements with environmental risk factors to identify the occurrence of porcine epidemic diarrhea virus (PEDV) outbreaks. Using weekly farm-level incidence data from 332 sow farms, we applied machine-learning algorithms to quantify associations between risk factors and PEDV outbreaks with the ultimate goal of training predictive models and to identify the most important factors associated with PEDV occurrence. Our best algorithm was able to correctly predict whether an outbreak occurred during one-week periods with >80% accuracy. The most important predictors included pig movements into neighboring farms. Other important neighborhood attributes included hog density, environmental and weather factors such as vegetation, wind speed, temperature, and precipitation, and topographical features such as slope. Our neighborhood-based approach allowed us to simultaneously capture disease risks associated with long-distance animal movement as well as local spatial dynamics. The model presented here forms the foundation for near real-time disease mapping and will advance disease surveillance and control for endemic swine pathogens in the United States.

Epidemiological models of the spread of pathogens in livestock populations primarily focus on direct contact between farms based on animal movement data, and in some cases, local spatial spread based on proximity between premises. The roles of other types of indirect contact among farms is rarely accounted for. In addition, data on animal movements is seldom available in the United States. However, the spread of porcine epidemic diarrhea virus (PEDv) in U.S. swine represents one of the best documented emergences of a highly infectious pathogen in the U.S. livestock industry, providing an opportunity to parameterize models of pathogen spread via direct and indirect transmission mechanisms in swine. Using observed data on pig movements during the initial phase of the PEDv epidemic, we developed a network-based and spatially explicit epidemiological model that simulates the spread of PEDv via both indirect and direct movement-related contact in order to answer unresolved questions concerning factors facilitating between-farm transmission. By modifying the likelihood of each transmission mechanism and fitting this model to observed epidemiological dynamics, our results suggest that between-farm transmission was primarily driven by direct mechanisms related to animal movement and indirect mechanisms related to local spatial spread based on geographic proximity. However, other forms of indirect transmission among farms, including contact via contaminated vehicles and feed, were responsible for high consequence transmission events resulting in the introduction of the virus into new geographic areas. This research is among the first reports of farm-level animal movements in the U.S. swine industry and, to our knowledge, represents the first epidemiological model of commercial U.S. swine using actual data on farm-level animal movement., (Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.)