Porcine reproductive and respiratory syndrome (PRRS) is one of the most economically important diseases affecting the swine industry. The main objective of this study was to assess whether sow farm distance to slaughterhouses and meteorological variables were associated with PRRS outbreaks. This case-control study paired 104 sow farms with or without a reported PRRS outbreak (N = 208) during the same period. Data on the distance to the closest slaughterhouse, swine density, presence of an air filtration system, or a neighboring farm, and weather conditions were collected, and a multivariable conditional logistic regression model was created to investigate the association between variables of interest and the occurrence of a PRRS outbreak. Swine density, presence of an air filtration system, presence of a neighboring farm, and PRRS herd-level status before the outbreak were associated with the occurrence of PRRS outbreaks. Farms in areas with higher swine density and nearby swine farms had increased odds of reporting an outbreak compared to farms in low swine density areas and farms having no neighbors. Under the conditions of this study, none of the meteorological variables or the distance to the closest slaughterhouse were associated with occurrence of PRRS outbreaks.

The reporting and monitoring of swine enteric coronavirus diseases (SECD), including porcine epidemic diarrhea virus and porcine delta coronavirus, in the United States have been challenging because of the initial absence of a regulatory framework and the emerging nature of these diseases. The National Animal Health Laboratory Network, the Emergency Management and Response System, and the Swine Health Monitoring Project were used to monitor the disease situation between May 2013 and March 2015. Important differences existed between and among them in terms of nature and extent of reporting. Here, we assess the implementation of these systems from different perspectives, including a description and comparison of collected data, disease metrics, usefulness, simplicity, flexibility, acceptability, representativeness, timeliness, and stability. This assessment demonstrates the limitations that the absence of premises identification imposes on certain animal health surveillance and response databases, and the importance of federally regulated frameworks in collecting accurate information in a timely manner. This study also demonstrates the value that the voluntary and producer-organized systems may have in monitoring emerging diseases. The results from all three data sources help to establish the baseline information on SECD epidemiological dynamics after almost 3 years of disease occurrence in the country.

Although vaccination is the main measure to control influenza A virus (IAV) in swine, there is limited information on the efficacy of sow vaccination on reducing IAV infections in pigs at weaning. We assessed the effect of sow vaccination on IAV infection in pigs at weaning in a cohort of 52 breeding herds studied prospectively. Herds were voluntarily enrolled according to their IAV history, sow vaccination protocol and monitored during six months (prospective longitudinal study). On each herd, nasal swabs were collected monthly from 30 pigs at weaning and tested for IAV by RT-PCR. IAV was detected in 25% (75/305) of sampling events. Of 9,150 nasal swab pools (3 individual nasal swabs/pool), 15% (458/3050) of pools tested IAV positive. IAV infections in pigs at weaning were lower in vaccinated herds compared to non-vaccinated ones. Moreover, no significant differences were seen between prefarrow and whole herd protocols, or the use of commercial versus autogenous IAV vaccines. Prefarrow and whole herd vaccination protocols reduced the odds of groups testing IAV positive at weaning in comparison with no vaccination. Our results are relevant when considering implementation of sow vaccination to control influenza infections in pigs at weaning and, hence, minimize transmission to growing pigs and other farms.

Control of Mycoplasma hyorhinis (M. hyorhinis) associated disease is currently hindered by limited knowledge of the epidemiology and ecology of this organism. A prospective longitudinal investigation was conducted to determine the dynamics of M. hyorhinis colonization in two swine production systems. In each system (A, B), 51 young sows (parities 1, 2) and 56 older sows (>parity 2) were selected at farrowing and tested by qPCR of nasal swabs and for antibodies by serum ELISA. From each sow, a piglet was randomly selected, and nasal and serum samples were collected at birth, weaning, and 10 days post-weaning. Two further samplings were performed in the nursery and finishing stages during the high-risk periods for M. hyorhinis-associated disease, and 12 pigs were euthanized and necropsied at these later sampling events. The prevalence of M. hyorhinis colonization in sows was low (<5%). No associations were found between sow parity or sow serum titer and piglet nasal colonization at either birth or weaning. In contrast to the low prevalence (0.95-2.70%) observed in piglets pre-weaning, most pigs became colonized during the first four weeks after weaning and remained positive throughout the nursery and finishing stages. The detection of M. hyorhinis in oral fluids followed similar patterns as those observed using nasal swabs. ELISA results showed decreased detection of maternal antibodies at around 3 weeks of age and a subsequent increase after natural exposure. The role of M. hyorhinis in polyserositis and arthritis was demonstrated in these two herds. Establishing the temporal dynamics of exposure and infection with M. hyorhinis in pigs will enable more strategic implementation of intervention strategies in affected herds.

A sampling technique has been validated to monitor porcine reproductive and respiratory syndrome virus 2 (PRRSV-2) using the serosanguinous exudate known as processing fluids (PFs) that accumulate from tissues obtained during tail docking and castration. PFs are an aggregate sample of large numbers of piglets and litters. However, little is known about the effect of litter aggregation on the ability of PCR to correctly classify an aggregated PF sample as positive. We evaluated both the effect of litter aggregation and of PF pooling on PCR detection. We estimated that aggregation of at least 50 litters was possible when a pig with a Ct value of ~22 was present in the sample, and aggregation of up to 40 litters was possible when there was a sample with a Ct value of ~33. Pooling did not affect PCR detection when initial Ct values of 20 and 25 were assessed. However, in litters with initial Ct values of ≥30, the amount of pooling should be reduced. Our results provide producers and practitioners with a general framework to interpret more accurately the results of their PRRSV-2 surveillance programs using PF.

There is a need to develop cost effective approaches to sample large populations in particular to determine the disease status of pigs prior to weaning. In this study we assessed the presence of the porcine reproductive and respiratory syndrome virus (PRRSV) in the environment (surfaces and air) of farrowing rooms, and udder skin of lactating sows as an indirect measure of piglet PRRSV status. Samples were collected at processing and weaning every three weeks for 23 weeks after a PRRSV outbreak was diagnosed in a swine breeding herd. PRRSV was detected at processing in udder skin wipes, environmental wipes and airborne deposited particle samples up to 14 weeks post outbreak and at weaning in udder skin wipes up to 17 weeks post outbreak. Similar sensitivities were observed for udder skin wipes (43% [95% CI: 23%-66%]) and surface wipes (57% [95% CI: 34%-77%]) when compared to serum at the litter level from piglets at processing. PRRSV was detected in the environment and the udder skin of lactating sows, which indicates that aggregate samples of the environment or lactating sows may be used to evaluate the PRRSV status of the herd in pigs prior to weaning. However, the use of environmental samples to detect PRRSV by RT-PCR should not be used as the single method to assess the PRRSV status at the litter level. Furthermore, our findings also highlight potential sources of PRRSV infection for piglets in breeding herds.

Collection of serum samples of pigs at weaning to monitor for porcine reproductive and respiratory syndrome virus (PRRSV) has become a common practice to determine PRRSV herd infection status. Diagnostic sensitivity of this practice is low in herds undergoing PRRSV elimination once prevalence of infection is near zero. Thus, the goal of this study was to characterize the dynamics of PRRSV infection in 3 day-old pigs overtime using serum and serosanguineous fluids obtained as part of castration and tail docking practices (processing fluids (PF)). Secondary goal was to estimate sensitivity and specificity of PF in the 3 day old population. A 6000 breed-to-wean sow herd was monitored every three weeks for 23 weeks after a PRRSV outbreak by collecting both PF and individual serum samples from all pigs in the selected litters. Out of the 77 litters tested, 23 (29.8%) were identified as positive using the PF and the serum samples, with a Cohen's kappa statistic of 0.81 (95% CI: 0.59-1) between the results obtained in each sample type. The sensitivity and specificity of the PF relative to the results in serum was 87% (95% CI: 66%-97%) and 94% (95% CI: 85%-99%) respectively. The percentage of PRRSV positive litters decreased over time and litters from gilts were more likely to test positive than those from older sows. Overall, the study demonstrates that PF can be a convenient and reliable specimen to monitor PRRSV infection in breeding herds.

The trial objective was to compare the performance and animal health parameters of pigs raised according to one of 3 antibiotic (AB) protocols: standard AB medication consisting of mass treatment on days 4 and 21 and judicious AB therapy given therapeutically thereafter as group medication in water and feed or by individual injection (group T1, N = 702); modified AB medication identical to group T1 but with mass treatment only on day 4 and without subsequent therapeutic feed medication (group T2, N = 675); or an antibiotic-free (ABF) regimen (group T3, N = 702). All pigs were vaccinated with a modified-live porcine reproductive and respiratory syndrome virus (PRRSV) vaccine 3 days after weaning. Using a seeder pig model to mimic endemic field infection dynamics, pigs were contact-challenged with virulent PRRSV lineage 1 strain 174 four weeks after vaccination. At finishing, average daily gain (ADG) and mean feed conversion ratio (FCR) were significantly better (p ≤ 0.05) for the T1 and T2 groups compared to the T3 group. There were no significant differences in post-weaning ADG and FCR between the T1 and T2 groups. Mortality and removals significantly favored (p ≤0.05) the T1 and T2 groups (20.94% and 24.89%, respectively) versus the T3 group (57.98%). Net revenue per pig was $105.43, $98.79, and $33.81 for the T1, T2 and T3 groups, respectively. Under the conditions of this study, these results indicate that in a PRRSV-endemic setting involving bacterial co-infections, an ABF production strategy may leave pigs at considerable risk of exposure to severe clinical disease and that judicious use of antibiotics can significantly improve animal health.

Breed-to-wean pig farms play an important role in spreading influenza A virus (IAV) because suckling piglets maintain, diversify and transmit IAV at weaning to other farms. Understanding the nature and extent of which farm factors drive IAV infection in piglets is a prerequisite to reduce the burden of influenza in swine. We evaluated the association between IAV infection in piglets at weaning and farm factors including farm features, herd management practices and gilt- and piglet-specific management procedures performed at the farm. Voluntarily enrolled breed-to-wean farms (n = 83) agreed to share IAV diagnostic testing and farm data from July 2011 through March 2017 including data obtained via the administration of a survey. There were 23% IAV RT-PCR positive samples of the 12,814 samples submitted for IAV testing within 2989 diagnostic submissions with 30% positive submissions. Among all the factors evaluated (n = 24), and considering the season-adjusted multivariable analysis, only sow IAV vaccination and gilt IAV status at entry significantly reduced (p-value<0.05) IAV infections in piglets at weaning. Results from this study indicate that veterinarians and producers could manage these identified factors to reduce the burden of influenza in piglets prior to wean and perhaps, reduce the spread of IAV to other farms and people.

Porcine reproductive and respiratory syndrome (PRRS) is an endemic disease causing important economic losses to the US swine industry. The complex epidemiology of the disease, along with the diverse clinical outputs observed in different types of infected farms, have hampered efforts to quantify PRRS’ impact on production over time. We measured the impact of PRRS on the production of weaned pigs using a log-linear fixed effects model to evaluate longitudinal data collected from 16 sow farms belonging to a specific firm. We measured seven additional indicators of farm performance to gain insight into disease dynamics. We used pre-outbreak longitudinal data to establish a baseline that was then used to estimate the decrease in production. A significant rise of abortions in the week before the outbreak was reported was the strongest signal of PRRSV activity. In addition, production declined slightly one week before the outbreak and then fell markedly until weeks 5 and 6 post-outbreak. Recovery was not monotonic, cycling gently around a rising trend. At the end of the study period (35 weeks post-outbreak), neither the production of weaned pigs nor any of the performance indicators had fully recovered to baseline levels. This result suggests PRSS outbreaks may last longer than has been found in most other studies. We assessed PRRS’ effect on farm efficiency as measured by changes in sow production of weaned pigs per year. We translated production losses into revenue losses assuming an average market price of $45.2/weaned pig. We estimate that the average PRSS outbreak reduced production by approximately 7.4%, relative to annual output in the absence of an outbreak. PRRS reduced production by 1.92 weaned pigs per sow when adjusted to an annual basis. This decrease is substantially larger than the 1.44 decrease of weaned pigs per sow/year reported elsewhere.

Porcine reproductive and respiratory syndrome virus (PRRSv) outbreaks cause significant financial losses to the U.S. swine industry, where the pathogen is endemic. Seasonal increases in the number of outbreaks are typically observed using PRRSv epidemic curves. However, the nature and extent to which demographic and environmental factors influence the risk for PRRSv outbreaks in the country remains unclear. The objective of this study was to develop risk maps for PRRSv outbreaks across the United States (U.S.) and compare ecological dynamics of the disease in five of the most important swine production regions of the country. This study integrates spatial information regarding PRRSv surveillance with relevant demographic and environmental factors collected between 2009 and 2016. We used presence-only Maximum Entropy (Maxent), a species distribution modeling approach, to model the spatial risk of PRRSv in swine populations. Data fitted the selected model relatively well when the modeling approach was conducted by region (training and testing AUCs<0.75). All of the Maxent models selected identified high-risk areas, with probabilities greater than 0.5. The relative contribution of pig density to PRRSv risk was highest in pig-densely populated areas (Minnesota, Iowa and North Carolina), whereas climate and land cover were important in areas with relatively low pig densities (Illinois, Indiana, South Dakota, Nebraska, Kansas, Oklahoma, Colorado, and Texas). Although many previous studies associated the risk of PRRSv with high pig density and climatic factors, the study here quantifies, for the first time in the peer-reviewed literature, the spatial variation and relative contribution of these factors across different swine production regions in the U.S. The results will help in the design and implement of early detection, prevention, and control strategies for one of the most devastating diseases affecting the swine industry in the U.S.

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.

Porcine reproductive and respiratory syndrome (PRRS) is, arguably, the most impactful disease for the North American swine industry, due to its known considerable economic losses. The Swine Health Monitoring Project (SHMP) monitors and reports weekly new PRRS cases in 766 sow herds across the US. The time-dependent reproduction number (TD-R) is a measure of a pathogen’s transmissibility. It may serve to capture and report PRRS virus (PRRSV) spread at the regional and system levels. The primary objective of the study here was to estimate the TD-R values for PRRSV using regional and system-level PRRS data, and to contrast it with commonly used metrics of disease, such as incidence estimates and space–time clusters. The second objective was to test whether the estimated TD-Rs were homogenous across four US regions. Retrospective monthly incidence data (2009–2016) were available from the SHMP. The dataset was divided into four regions based on location of participants, and demographic and environmental features, namely, South East (North Carolina), Upper Midwest East (UME, Minnesota/Iowa), Upper Midwest West (Nebraska/South Dakota), and South (Oklahoma panhandle). Generation time distributions were fit to incidence data for each region, and used to calculate the TD-Rs. The Kruskal–Wallis test was used to determine whether the median TD-Rs differed across the four areas. Furthermore, we used a space–time permutation model to assess spatial–temporal patterns for the four regions. Results showed TD-Rs were right skewed with median values close to “1” across all regions, confirming that PRRS has an overall endemic nature. Variation in the TD-R patterns was noted across regions and production systems. Statistically significant periods of PRRSV spread (TD-R > 1) were identified for all regions except UME. A minimum of three space–time clusters were detected for all regions considering the time period examined herein; and their overlap with “spreader events” identified by the TD-R method varied according to region. TD-Rs may help to measure PRRS spread to understand, in quantitative terms, disease spread, and, ultimately, support the design, implementation, and monitoring of interventions aimed at mitigating the impact of PRRSV spread in the US.

The increasing availability and complexity of data has led to new opportunities and challenges in veterinary epidemiology around how to translate abundant, diverse, and rapidly growing “big” data into meaningful insights for animal health. Big data analytics are used to understand health risks and minimize the impact of adverse animal health issues through identifying high-risk populations, combining data or processes acting at multiple scales through epidemiological modeling approaches, and harnessing high velocity data to monitor animal health trends and detect emerging health threats. The advent of big data requires the incorporation of new skills into veterinary epidemiology training, including, for example, machine learning and coding, to prepare a new generation of scientists and practitioners to engage with big data. Establishing pipelines to analyze big data in near real-time is the next step for progressing from simply having “big data” to create “smart data,” with the objective of improving understanding of health risks, effectiveness of management and policy decisions, and ultimately preventing or at least minimizing the impact of adverse animal health issues.

Porcine reproductive and respiratory syndrome virus (PRRSv) infection causes a devastating economic impact to the swine industry. Active surveillance is routinely conducted in many swine herds to demonstrate freedom from PRRSv infection. The design of efficient active surveillance sampling schemes is challenging because optimum surveillance strategies may differ depending on infection status, herd structure, management, or resources for conducting sampling. Here, we present an open web-based application, named ‘OptisampleTM’, designed to optimize herd sampling strategies to substantiate freedom of infection considering also costs of testing. In addition to herd size, expected prevalence, test sensitivity, and desired level of confidence, the model takes into account the presumed risk of pathogen introduction between samples, the structure of the herd, and the process to select the samples over time. We illustrate the functionality and capacity of ‘OptisampleTM’ through its application to active surveillance of PRRSv in hypothetical swine herds under disparate epidemiological situations. Diverse sampling schemes were simulated and compared for each herd to identify effective strategies at low costs. The model results show that to demonstrate freedom from disease, it is important to consider both the epidemiological situation of the herd and the sample selected. The approach illustrated here for PRRSv may be easily extended to other animal disease surveillance systems using the web-based application available at http://stemma.ahc.umn.edu/optisample.

The US swine industry has been impaired over the last 25 years by the far-reaching financial losses caused by the porcine reproductive and respiratory syndrome (PRRS). Here, we explored the relations between the spatial risk of PRRS outbreaks and its phylodynamic history in the U.S during 1998–2016 using ORF5 sequences collected from swine farms in the Midwest region. We used maximum entropy and Bayesian phylodynamic models to generate risk maps for PRRS outbreaks and reconstructed the evolutionary history of three selected phylogenetic clades (A, B and C). High-risk areas for PRRS were best-predicted by pig density and climate seasonality and included Minnesota, Iowa and South Dakota. Phylodynamic models demonstrated that the geographical spread of the three clades followed a heterogeneous spatial diffusion process. Furthermore, PRRS viruses were characterized by typical seasonality in their population size. However, endemic strains were characterized by a substantially slower population growth and evolutionary rates, as well as smaller spatial dispersal rates when compared to emerging strains. We demonstrated the prospects of combining inferences derived from two unique analytical methods to inform decisions related to risk-based interventions of an important pathogen affecting one of the largest food animal industries in the world.

Porcine reproductive and respiratory syndrome (PRRS) causes far-reaching financial losses to infected countries and regions, including the U.S. The Dr. Morrison's Swine Health Monitoring Program (MSHMP) is a voluntary initiative in which producers and veterinarians share sow farm PRRS status weekly to contribute to the understanding, in quantitative terms, of PRRS epidemiological dynamics and, ultimately, to support its control in the U.S. Here, we offer a review of a variety of analytic tools that were applied to MSHMP data to assess disease dynamics in quantitative terms to support the decision-making process for veterinarians and producers. Use of those methods has helped the U.S. swine industry to quantify the cyclical patterns of PRRS, to describe the impact that emerging pathogens has had on that pattern, to identify the nature and extent at which environmental factors (e.g., precipitation or land cover) influence PRRS risk, to identify PRRS virus emerging strains, and to assess the influence that voluntary reporting has on disease control. Results from the numerous studies reviewed here provide important insights into PRRS epidemiology that help to create the foundations for a near real-time prediction of disease risk, and, ultimately, will contribute to support the prevention and control of, arguably, one of the most devastating diseases affecting the North American swine industry. The review also demonstrates how different approaches to analyze and visualize the data may help to add value to the routine collection of surveillance data and support infectious animal disease control.

Porcine reproductive and respiratory syndrome (PRRS) is, arguably, the most impactful disease on the North American swine industry. The Swine Health Monitoring Project (SHMP) is a national volunteer initiative aimed at monitoring incidence and, ultimately, supporting swine disease control, including PRRS. Data collected through the SHMP currently represents approximately 42% of the sow population of the United States. The objective of the study here was to investigate the association between geographical factors (including land elevation, and land coverage) and PRRS incidence as recorded in the SHMP. Weekly PRRS status data from sites participating in the SHMP from 2009 to 2016 (n = 706) was assessed. Number of PRRS outbreaks, years of participation in the SHMP, and site location were collected from the SHMP database. Environmental features hypothesized to influence PRRS risk included land coverage (cultivated areas, shrubs and trees), land altitude (in meters above sea level) and land slope (in degrees compared to surrounding areas). Other risk factors considered included region, production system to which the site belonged, herd size, and swine density in the area in which the site was located. Land-related variables and pig density were captured in raster format from a number of sources and extracted to points (farm locations). A mixed-effects Poisson regression model was built; and dependence among sites that belonged to a given production system was accounted for using a random effect at the system level. The annual mean and median number of outbreaks per farm was 1.38 (SD: 1.6), and 1 (IQR: 2.0), respectively. The maximum annual number of outbreaks per farm was 9, and approximately 40% of the farms did not report any outbreak. Results from the final multivariable model suggested that increments of swine density and herd size increased the risk for PRRS outbreaks (P < 0.01). Even though altitude (meters above sea level) was not significant in the final model, farms located in terrains with a slope of 9% or higher had lower rates of PRRS outbreaks compared to farms located in terrains with slopes lower than 2% (P < 0.01). Finally, being located in an area of shrubs/ herbaceous cover and trees lowered the incidence rate of PRRS outbreaks compared to being located in cultivated/ managed areas (P < 0.05). In conclusion, highly inclined terrains were associated with fewer PRRS outbreaks in US sow farms, as was the presence of shrubs and trees when compared to cultivated/ managed areas. Influence of terrain characteristics on spread of airborne diseases, such as PRRS, may help to predicting disease risk, and effective planning of measures intended to mitigate and prevent risk of infection.

Influenza is a costly disease for pig producers and understanding its epidemiology is critical to control it. In this study, we aimed to estimate the herd-level prevalence and seasonality of influenza in breed-to-wean pig farms, evaluate the correlation between influenza herd-level prevalence and meteorological conditions, and characterize influenza genetic diversity over time. A cohort of 34 breed-to-wean farms with monthly influenza status obtained over a 5-year period in piglets prior to wean was selected. A farm was considered positive in a given month if at least one oral fluid tested influenza positive by reverse transcriptase polymerase chain reaction. Influenza seasonality was assessed combining autoregressive integrated moving average (ARIMA) models with trigonometric functions as covariates. Meteorological conditions were gathered from local land-based weather stations, monthly aggregated and correlated with influenza herd-level prevalence. Influenza herd-level prevalence had a median of 28% with a range from 7 to 57% and followed a cyclical pattern with levels increasing during fall, peaking in both early winter (December) and late spring (May), and decreasing in summer. Influenza herd-level prevalence was correlated with mean outdoor air absolute humidity (AH) and temperature. Influenza genetic diversity was substantial over time with influenza isolates belonging to 10 distinct clades from which H1 delta 1 and H1 gamma 1 were the most common. Twenty-one percent of farms had three different clades co-circulating over time, 18% of farms had two clades, and 41% of farms had one clade. In summary, our study showed that influenza had a cyclical pattern explained in part by air AH and temperature changes over time, and highlighted the importance of active surveillance to identify high-risk periods when strategic control measures for influenza could be implemented.