Disentangling transport movement patterns of trucks either transporting pigs or while empty within a swine production system before and during the COVID-19 epidemic.

Transport of pigs between sites occurs frequently as part of genetic improvement and age segregation. However, a lack of transport biosecurity could have catastrophic implications if not managed properly as disease spread would be imminent. However, there is a lack of a comprehensive study of vehicle movement trends within swine systems in the Midwest. In this study, we aimed to describe and characterize vehicle movement patterns within one large Midwest swine system representative of modern pig production to understand movement trends and proxies for biosecurity compliance and identify potential risky behaviors that may result in a higher risk for infectious disease spread. Geolocation tracking devices recorded vehicle movements of a subset of trucks and trailers from a production system every 5 min and every time tracks entered a landmark between January 2019 and December 2020, before and during the COVID-19 pandemic. We described 6,213 transport records from 12 vehicles controlled by the company. In total, 114 predefined landmarks were included during the study period, representing 5 categories of farms and truck wash facilities. The results showed that trucks completed the majority (76.4%, 2,111/2,762) of the recorded movements. The seasonal distribution of incoming movements was similar across years (P > 0.05), while the 2019 winter and summer seasons showed higher incoming movements to sow farms than any other season, year, or production type (P < 0.05). More than half of the in-movements recorded occurred within the triad of sow farms, wean-to-market stage, and truck wash facilities. Overall, time spent at each landmark was 9.08% higher in 2020 than in 2019, without seasonal highlights, but with a notably higher time spent at truck wash facilities than any other type of landmark. Network analyses showed high connectivity among farms with identifiable clusters in the network. Furthermore, we observed a decrease in connectivity in 2020 compared with 2019, as indicated by the majority of network parameter values. Further network analysis will be needed to understand its impact on disease spread and control. However, the description and quantification of movement trends reported in this study provide findings that might be the basis for targeting infectious disease surveillance and control.

Assessing the role of sow parity on PRRSv detection by RT-qPCR through weekly processing fluids monitoring in breeding herds.

The use of processing fluids to monitor the breeding herd's porcine reproductive and respiratory syndrome (PRRS) status has gained industry acceptance. However, little is known about PRRS virus RT-qPCR detection dynamics in processing fluids and factors that may contribute to maintain PRRS virus in the herd after an outbreak. This study aimed to describe weekly RT-qPCR processing fluid results in breeding herds after an outbreak and to evaluate the proportion of RT-qPCR positive results among parity groups. Processing tissues of 15 first parity (P1), 15 second parity (P2), and 15 third parity or higher (P3+) litters (parity groups) were collected weekly for between 19 and 46 weeks in nine breeding herds. Processing fluids were aggregated, and RT-qPCR tested by parity group weekly. Additionally, a subset of 743 processing fluid samples of litters that formed 50 parity groups, as previously described, were RT-qPCR tested individually at the litter level. The agreement between RT-qPCR results of processing fluid samples of parity groups (15 litters) and results based on individual litter testing was assessed using overall percent of agreement, Kappa statistic, and McNemar test. The association between RT-qPCR results and the parity group was evaluated using a generalized estimating equations model, after accounting for the effects of sampling week, breeding herd PRRS control strategy (i.e., open to replacements v/s closed) and herd. An autoregressive correlation structure was used to account for the repeated samplings within a herd in time. The overall agreement was 98 %, and Kappa statistic 0.955 (McNemar p = 1.0). Sensitivity of parity group processing fluid samples was estimated at 100 % (95 % CI 89-100 %), while specificity was estimated at 94 % (95 % CI 71-100 %). Although P1 aggregated litters had on average a higher proportion of RT-qPCR positive results from outbreak week 25 onwards, the proportion was not significantly different to the one observed for P2 and P3+ aggregated litters (p > 0.13). Additionally, herds that interrupted gilt entry had lower odds of PRRS RT-qPCR positivity than herds that continued entering gilts (OR = 0.35, 95 % CI 0.16-0.78). PRRS virus persistence in processing fluids was not affected by the sow parity effect in most of the breeding herds studied. No evidence of disagreement between RT-qPCR results of an aggregated sample of 15 litters and those of individual litters was observed. This level of litter aggregation testing strategy may be of particular use at the last stages of an elimination program under low PRRS virus prevalence.

Forecasting viral disease outbreaks at the farm-level for commercial sow farms in the U.S.

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.

Temporal Dynamics of Co-circulating Lineages of Porcine Reproductive and Respiratory Syndrome Virus.

Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) is the most important endemic pathogen in the U.S. swine industry. Despite control efforts involving improved biosecurity and different vaccination protocols, the virus continues to circulate and evolve. One of the foremost challenges in its control is high levels of genetic and antigenic diversity. Here, we quantify the co-circulation, emergence and sequential turnover of multiple PRRSV lineages in a single swine-producing region in the United States over a span of 9 years (2009-2017). By classifying over 4,000 PRRSV sequences (open-reading frame 5) into phylogenetic lineages and sub-lineages, we document the ongoing diversification and temporal dynamics of the PRRSV population, including the rapid emergence of a novel sub-lineage that appeared to be absent globally pre-2008. In addition, lineage 9 was the most prevalent lineage from 2009 to 2010, but its occurrence fell to 0.5% of all sequences identified per year after 2014, coinciding with the emergence or re-emergence of lineage 1 as the dominant lineage. The sequential dominance of different lineages, as well as three different sub-lineages within lineage 1, is consistent with the immune-mediated selection hypothesis for the sequential turnover in the dominant lineage. As host populations build immunity through natural infection or vaccination toward the most common variant, this dominant (sub-) lineage may be replaced by an emerging variant to which the population is more susceptible. An analysis of patterns of non- synonymous and synonymous mutations revealed evidence of positive selection on immunologically important regions of the genome, further supporting the potential that immune-mediated selection shapes the evolutionary and epidemiological dynamics for this virus. This has important implications for patterns of emergence and re-emergence of genetic variants of PRRSV that have negative impacts on the swine industry. Constant surveillance on PRRSV occurrence is crucial to a better understanding of the epidemiological and evolutionary dynamics of co-circulating viral lineages. Further studies utilizing whole genome sequencing and exploring the extent of cross-immunity between heterologous PRRS viruses could shed further light on PRRSV immunological response and aid in developing strategies that might be able to diminish disease impact.

Detection of Mycoplasma hyopneumoniae in piglet processing fluids.

Processing fluid (PF) is a sample type composed of fluids obtained from testicles and tails as the product of piglet processing. Mycoplasma hyopneumoniae is a bacterium that colonises the respiratory tract of pigs and has rarely been detected in tissues outside the respiratory system. No data exist in the literature regarding detection of M hyopneumoniae in PF or its use for herd monitoring of this pathogen. The main goal of this study was to evaluate the feasibility of detecting M hyopneumoniae in PF. Testicles and tails of 21 conveniently selected litters from a commercial sow farm were collected, by litter, and tested for M hyopneumoniae by real time-PCR. Daily aggregated processing tissues were collected for a two-month period to assess the detection of M hyopneumoniae in PF. The comparison in the percentage of positive samples in fluids from testicles (38 per cent, 8/21) or tails (4.8 per cent, 1/21) was significantly different (P=0.023). The percentage of daily aggregated PF with cycle threshold values up to 37 was 52.9 per cent (9/17) and 26.7 per cent (4/15) for December and January, respectively. Overall, these data show detection of M hyopneumoniae in PF for the first time and points at the potential use of this sample for monitoring of this bacterium in breeding farms.