Recording PRRSv RFLP and sequences will provide better insights into the epidemiology of the disease at local, state and national level.
Building a RFLP database will allow us to assess which factors could be involved or related with the emergence of a new RFLP.
The predominant pattern RFLP in this quarterly review is the 1-7-4.
In the first quarter of the 2018/2019 incidence year, 20 breaks affecting 12 production systems were reported. Out of these, 4 occurred in July, 13 in August and 3 in September.
Of those 20 farms, three had a break while still being status 1, one was status 2 in the process of eliminating the disease (not using any immunization protocol at that point), 6 were using field virus as the acclimatization protocol (2fvi), 8 were using vaccine (2vx), one was provisionally negative (status 3) and one broke from a status 4 after being almost 4 years completely negative (see figure below).
The distribution of the breaks is wide and affects different states. Thus, we had 6, 1, 4, 1, 4, 2, 1 and 1 break in the states of IA, IN, MN, MO, NC, NE, OK and PA, respectively. The closest 2 farms that broke were 1.2 miles apart, belonged to the same company and had the break a week from each other (no sequences was provided).
Eight out of the 20 breaks reported were accompanied by the associated RFLP. The predominant (4 out of 8) RFLP pattern since July is 1-7-4. Iowa was the state with the highest number of 1-7-4 cases.
There is significant within farm PRRS time-to-stability variation.
Several factors contribute to PRRS time-to-stability variability; however, there is still a significant amount of unexplained variability.
The role of within farm management practices and internal biosecurity measures should be further explored.
Porcine reproductive and respiratory syndrome (PRRS) stability is reached when no evidence of infection is observed in wean-age piglets. Sample size to detect PRRS virus in wean-age piglets usually involves blood sampling of 30 piglets, at least four times, 30 days apart (Holtkamp et al., 2011). The cumulative time from the intervention (i.e. whole herd exposure, herd closure) to PRRS stability is usually referred to as time-to-stability (TTS).
Here we summarize differences in TTS in MSHMP participating farms located in the Midwest that have had at least two PRRS outbreaks.
Six systems that are similar in the way they test to classify a herd as stable were selected for inclusion in the study. PRRS outbreaks reported from 2011 to 2017 were used for analysis.
TTS was defined as the time period from the date of outbreak reporting to the date when PRRS stability was reported (last consecutive negative PCR result). To assess the variability in TTS, only farms that had at least two PRRS outbreaks were selected.
Overall, 133 PRRS outbreaks in 53 farms were recorded withtwo, three, four and five outbreaks in 35, 11, 5, 2 farms, respectively. The median TTS standard deviation of PRRS outbreaks within the same farm was 12 weeks (minimum = 0 weeks, maximum=88 weeks).
After accounting for the effect of the intervention using MLV or FVI, the RFLP pattern of the virus associated with the outbreak and previous PRRS outbreaks in the farm, the PRRS time-to-stability correlation of outbreaks in the same farm and system was only 1.2%.
In other words, TTS of two given outbreaks in the same farm were not correlated indicating that TTS within farm is highly variable.
There is a high TTS variability after a PRRS outbreak within the same farm that is not accounted for by the effect of the intervention used, the virus (i.e RFLP), previous PRRS outbreaks in the farm and system.
The multi-site pig production structure of the U.S. swine industry requires frequent movement of swine, making swine populations vulnerable to disease spread. This scenario becomes even more relevant in highly dense regions that concentrate thousands of pigs.
By targeting sites that play an important “connectivity” role such as gilt producing sites, prevention and control strategies for disease containment can be developed together with targeted surveillance for early detection of disease.
Swine movement data in three large production systems in the United States were analyzed to measure how a specific farm could influence a potential disease spread. Several network metrics were measured including:
the number of other farms to which a specific farm sent or received pigs,
the Mean Infection Potential (MIP), which measures potential incoming and outgoing infection chains.
For example, if a nursery farm received pigs from several sow farms and then sent pigs to multiple finisher farms, that farm would likely have a high MIP and could be called a “super-spreader” : a farm that could contribute to a high number of infections.
The study found that by directing disease interventions toward farms based on their MIP, the potential for infectious disease transmission in the production system can be substantially reduced. Interestingly, production type (sow, nursery, finishing, farrow-finish and wean-to-finish) did not seem to be a key determinant of the MIP.
When we really break it down, it’s all about incoming and outgoing contacts and the impact on risk. For more information about analysis of movement data, identifying super-spreaders farms and implications for disease control for farms in your system, contact Kim VanderWaal.
This study was conducted using data collected from the Morrison Swine Health Monitoring Project. The main objective of this study was to use time-series analysis to investigate whether yearly patterns commonly described for PRRS were in fact conserved across different U.S. states.
The 268 breeding herds enrolled in this project were the ones that participated in the MSHMP from July 2009 to October 2016. PPRS status of each farm was reported weekly following the AASV guidelines. The five states examined included Minnesota (MN), Iowa (IA), North Carolina (NC), Nebraska (NE), and Illinois (IL).
81 MN farms, 72 IA, 45 NC, 30 NE, 40 from IL, were enrolled in the study with a mean number of animals per site of 2,666; 3,543; 2,342; 4,041; and 4,018 respectively.
The main finding of this study was that PRRS seasonality varies according to geographical region, and the commonly referred “PRRS season” is not necessarily the only time of increase in disease incidence.
Another interesting finding from this study was the presence of an alternating trend for all examined states within of the U.S., except for the state of Iowa, the largest pork producing states in the country (approximately 31.4% of the total US hog and pig inventory), which had an increasing linear trend over the examined years.
In conclusion, PRRS seasonal patterns are not homogeneous across the U.S., with some important pork producing states having biannual PRRS peaks instead of the previously reported winter peak. Findings from this study highlight the importance of coordinating alternative control strategies in different regions considering the prevailing epidemiological patterns, and the need to reinforce strict biosecurity practices beyond the typically described “PRRS season”.
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.
Although our understanding of disease and control methods has improved in recent years, we continue to learn new features of PRRSV epidemiology in part thanks to the Morrison Swine Health Monitoring Project. One of the most recent questions that we have addressed based on enquires from MSHMP participants is whether PRRS incidence during the summer was higher in recent years (i.e. 2016-17) compared to previous years (i.e. 2009-15). We know that PRRSV outbreaks tend to have a seasonal pattern and that they are more frequent during the fall and winter, but we know little about the breaks that happen in the summer and spring.
In order to dig into this question, we analyzed MSHMP data from 2009 to 2017 which included 1,329 outbreaks. Of these, 66% of the breaks occurred during fall and winter and 14% and 20% of the breaks occurred during summer and spring, respectively. Although there were fewer breaks in the spring and summer, the number of breaks in warmer seasons was still significant which represents an on-going frustration to producers because the “PRRSV season” is supposed to be over.
As part of the analysis we learned that between 3% and 6% of the herds break yearly during the summer and spring seasons, respectively. This represents approximately 83 herds out of the 917 reporting in the MSHMP database. If we estimate that the average sow farm has 3,000 sows, then almost a quarter of a million sows break yearly during these two seasons.
Remember, although the risk of PRRSV introduction is lower during the spring and summer, PRRSV breaks still happen, so biosecurity efforts should not be decreased. PRRSV is a sneaky virus so keep your biosecurity up, even in the summer.
All of our collaborations with the National Hog Farmer can be found here.
This is our Friday rubric: every week a new Science Page from the Bob Morrison’s Swine Health Monitoring Project. The previous editions of the science page are available on our website.
This week, Dr. Cesar Corzo is giving us a summary of the 2017/2018 year.
Objective 1 – Disease incidence and monitoring
PRRS – Unfortunately 31% of the herds in the project broke with PRRS making it the third highest incidence in the MSHMP history. The epidemic initiated at the same time of the year following previous years’ pattern. As with previous years, we continue to see viral introduction into 1) status 4 breeding herds in low dense regions and 2) filtered sow herds reminding us that there continues to be unanswered questions from a transmission standpoint.
PEDv – The year ended at 8% (1% increase compared to the previous year) with a series of outbreaks occurring in 12 farms that had never been exposed to PEDv.
PDCoV – Even though we have not been including a graph we continue to monitor for this virus. There has been minimal activity.
SVV – Incidence of this virus remained low and did not follow the seasonal pattern seen in the previous 2 years.
Atypical CNS Cases – These viruses continue to be found in specific cases with no apparent trend.
Objective 2 – Prospective monitoring of PRRSv
PRRSv sequences continue to be collected building a library for MSHMP participant use. We have used this approach a few times while outbreak investigations have been conducted. We are currently conducting monitoring in a three-company based region detecting newly emerged viruses. On the other hand, the database is being analyzed in a way that provides epidemiological sense. We will report more on this in an upcoming report.
Objective 3 – Develop capacity to capture and analyze movement data
We have been able to generate a process to record movement data (i.e. starting and ending location,speed, trip duration) together with a visualization package in Google Earth. Although we have proved the concept we have faced technology challenges during the development phase and we are currently revisiting our approach.
Objective 4 – To expand participation of producers to allow all to be involved
Expansion continues with existing participants adding more farms. There have been other production systems that have either signed the
enrollment forms and are in the process of submitting their data or other production companies that have verbally agreed to join.
Even though small differences between both EWMAs exist, the EWMA of the original 13 participating systems is still a good indicator of the overall PRRS EWMA.
Questions from participants are always welcome and help us to provide answers and insights to all of you.
REMINDER: WHAT IS THE EWMA?
The Exponential Weighted Moving Average (EMWA) is a statistical method that averages data over time, continually decreasing the weight of data as it moves further back in time. An EWMA chart is particularly good at monitoring processes that drift over time and is used to detect small shifts in a trend.
In our project, EWMA is used to follow the evolution of the % of farms at risk that broke with PRRSV every week. EWMA incorporates all the weekly percentages recorded since the beginning of the project and gives less and less weight to the results as they are more removed in time. Therefore, the % of farms at risk that broke with PRRSV last week will have much more influence on the EMWA than the % of farms at risk that broke with PRRSV during the same week last year.
Results from this year’s comparison
EWMA 13 is still a good representation of the overall EWMA. The reason that the EWMA 13 is still representative may be because they cover a wide area of the States and they still represent a high percentage of the final EWMA. A minor difference occurred in 2017’s summer as some farms of the 13 experienced outbreaks. However, as we have discussed in previous science pages each state or region seems to have a different EWMA pattern.