Factors affecting porcine reproductive and respiratory syndrome virus time to stability in breeding herds in the midwestern US
Sanhueza et al.
The time needed to wean porcine reproductive and respiratory syndrome (PRRS) virus negative pigs consistently from a breeding herd after an outbreak is referred to as time‐to‐stability (TTS). TTS is an important measure to plan herd closure as well as to manage economic expectations. Weekly PRRS incidence data from 82 sow farms in six production systems located in the Midwestern United States were used for the analysis. The objective of this study was to evaluate the effect of recorded predictors on TTS in participant sow farms. The median TTS was 41.0 weeks (1st quartile 31.0 weeks–3rd quartile 55.0 weeks). In the final multivariable mixed‐effects Cox model, farms that experienced winter (hazard ratio (HR) 2.18, 95% confidence interval (CI) 1.28–3.70) and autumn (HR 1.91, 95% CI 1.16–3.13) PRRS outbreaks achieved stability sooner than farms that experienced PRRS outbreaks during summer. No statistically significant difference (p = 0.76) was observed between the TTS of farms that had a PRRS outbreak during spring and summer (HR 1.09, 95% CI 0.62–1.91). Additionally, farms that had a PRRS outbreak associated with a 1‐7‐4 restriction fragment length polymorphism (RFLP) cut pattern took significantly longer to achieve stability (HR 0.44, 95% CI 0.27–0.72) compared to farms which had a non‐1‐7‐4 PRRS outbreak. Finally, farms that had a previous PRRS outbreak within a year achieved stability sooner (HR 2.18, 95% CI 1.23–3.86) than farms that did not have a previous PRRS outbreak within a year. This study provides information that may result useful for planning herd closure and managing expectations about the time needed to wean PRRS virus negative pigs in breading herds according to the season of the year when the outbreak occurred and the RFLP cut pattern associated with the outbreak virus.
Assessment of area spread of porcine reproductive and respiratory syndrome (PRRS) virus in three clusters of swine farms
Andreia Arruda et al.
Despite decades of porcine reproductive and respiratory syndrome (PRRS) research, outbreaks with emerging and re‐emerging PRRS virus (PRRSV) strains are not uncommon in North America. The role of area spread, commonly referred but not limited to airborne transmission, in originating such outbreaks is currently unknown. The main objective of this study was to explore the role of area spread on the occurrence of new PRRSV cases by combining information on genetic similarity among recovered PRRSV isolate's open‐reading frame (ORF) 5 sequences and publicly available weather data. Three small regions were enrolled in the study for which high farm‐level participation rate was achieved, and swine sites within those regions were readily sampled after reporting of an outbreak in a sow farm. Oral fluid PCR testing was used to determine PRRSV status of farms, and wind roses were generated for assessment of prevailing wind directions during 2–14 days preceding the outbreak. Under the conditions of this study, the data did not support the area spread theory as the main cause for these outbreaks. We suggest that for future studies, analysis of animal movement and other links between farms such as personnel, equipment and sharing of service providers should be incorporated for better insights on source of the virus. Furthermore, the development of rapid and easy diagnostic methods for ruling out resident PRRSV is urgently needed.
Time-series analysis for porcine reproductive and respiratory syndrome in the United States
Andreia Arruda et al.
Industry-driven voluntary disease control programs for swine diseases emerged in North America in the early 2000’s, and, since then, those programs have been used for monitoring diseases of economic importance to swine producers. One example of such initiatives is Dr. Morrison’s Swine Health Monitoring Project, a nation-wide monitoring program for swine diseases including the porcine reproductive and respiratory syndrome (PRRS). PRRS has been extensively reported as a seasonal disease in the U.S., with predictable peaks that start in fall and are extended through the winter season. However, formal time series analysis stratified by geographic region has never been conducted for this important disease across the U.S. The main objective of this study was to use approximately seven years of PRRS incidence data in breeding swine herds to conduct time-series analysis in order to describe the temporal patterns of PRRS outbreaks at the farm level for five major swine-producing states across the U.S. including the states of Minnesota, Iowa, North Carolina, Nebraska and Illinois. Data was aggregated retrospectively at the week level for the number of herds containing animals actively shedding PRRS virus. Basic descriptive statistics were conducted followed by autoregressive integrated moving average (ARIMA) modelling, conducted separately for each of the above-mentioned states. Results showed that there was a difference in the nature of PRRS seasonality among states. Of note, when comparing states, the typical seasonal pattern previously described for PRRS could only be detected for farms located in the states of Minnesota, North Carolina and Nebraska. For the other two states, seasonal peaks every six months were detected within a year. In conclusion, we showed that epidemic patterns are not homogeneous across the U.S, with major peaks of disease occurring through the year. These findings highlight the importance of coordinating alternative control strategies in different regions considering the prevailing epidemiological patterns.
Monitoring breeding herd production data to detect PRRSV outbreaks
Gustavo Silva et al.
Porcine reproductive and respiratory syndrome virus (PRRSv) causes substantial economic impact due to significant losses in productivity. Thus, measuring changes in farm productivity before and after PRRS infection enables quantifying the production and economic impact of outbreaks. This study assessed the application of exponentially weighted moving average (EWMA), a statistical process control method, on selected production data (number of abortions, pre-weaning mortality rate and prenatal losses) to supplement PRRS surveillance programs by detecting significant deviations on productivity in a production system with 55,000 sows in 14 breed-to-wean herds in Minnesota, U.S.A. Weekly data from diagnostic monitoring program (available through the Morrison’s Swine Health Monitoring Project) implemented on the same herds was used as reference for PRRS status. The time-to-detect, percentage of early detection of PRRSv-associated productivity deviations, and relative sensitivity and specificity of the production data monitoring system were determined relative to the MSHMP. The time-to-detect deviations on productivity associated with PRRS outbreaks using the EWMA method was −4 to −1 weeks (interquartile range) for the number of abortions, 0–0 for preweaning mortality and −1 to 3 weeks for prenatal losses compared to the date it was reported in the MSHMP database. Overall, the models had high relative sensitivity (range 85.7–100%) and specificity (range 98.5%–99.6%) when comparing to the changes in PRRS status reported in the MSHMP database. In summary, the use of systematic data monitoring showed a high concordance compared to the MSHMP-reported outbreaks indicating that on-farm staff and veterinary oversight were efficient to detect PRRSv, but can be more efficient if they were monitoring closely the frequency of abortions. The systematic monitoring of production indicators using EWMA offers opportunity to standardize and semi-automate the detection of deviations on productivity associated with PRRS infection, offering opportunity to early detect outbreaks and/or to quantify the production losses attributed to PRRS infection.
Effect of immunologic solutions on sows and gilts on time to stability and production losses in breeding herds infected with 1-7-4 PRRSv
Daniel Linhares et al.
Porcine reproductive and respiratory syndrome virus (PRRSv) is an economically significant swine pathogen causing production losses in the global swine industry. Clinical impact depends on many factors including the virus itself. One method to sub-type PRRSv is using restriction fragment length polymorphism (RFLP). The RFLP pattern 1-7-4 emerged within the United States swine industry in 2014 and has become prevalent since then. This was a field study that prospectively followed 1-7-4-infected breeding herds (n=107) and compared time to stability (TTS), time to baseline production (TTBP) and total loss per 1000 sows between herds using modified-live virus vaccine (MLV) on sows and gilts (MLV-MLV), MLV on sows and MLV in addition to field virus exposure on gilts (MLV-MLV/FVE) or not deliberately exposing sows or gilts to PRRSv (Natural-Natural). Analyses were done in SAS 9.4 and results were adjusted by selected co-variates (duration of herd closure, number of previous PRRSv outbreaks of last 3 years, weaning frequency/week, gilt development unit location, herd size and production system). Survival analysis was conducted on TTS and TTBP and regression analysis on total loss. Herds in the Natural-Natural group achieved TTS and TTBP before other herds. Herds in the MLV-MLV/FVE had the longest TTS and TTBP. The total loss was numerically least in MLV-MLV herds (1194 pigs/1000 sows) compared to MLV/MLV-FVE (1810/1000 sows) and Natural-Natural (2671/1000 sows). This study provided additional information to assist veterinarians deciding between methods of exposure to manage PRRSv infection from breeding herds.
A review of quantitative tools used to assess the epidemiology of PRRS in US swine farms using HSHMP
Carles Vilalta et al.
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.
Estimation of time-dependent reproduction numbers for PRRS across different regions and production systems of the US
Andreia Arruda et al.
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.
Land altitude, slope and coverage as risk factors for PRRS outbreaks in the United States
Andreia Arruda et al.
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.
Surveillance of PRRSv in the United States using risk mapping and species distribution modeling
Moh Alkamis et al.
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.