NHF: Developing targeted disease surveillance and control plans

Our monthly collaboration with the National Hog Farmer continues; this month Dr. Kim VanderWaal shares her research regarding swine disease surveillance.

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.

Super spreader
Farm icon created by Ferran Brown for the Noun Project

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.

Time-series analysis for porcine reproductive and respiratory syndrome in the United States

Today, we are sharing an open-access publication from Dr. Andreia Arruda, Dr. Ana Alba and members of the MSHMP team in the journal PlosOne.

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.

Graphs showing the prevalence (black line) and upper and lower 95% confidence intervals (grey dotted lines) of PRRS virus positive farms for the five different U.S. states participating in this study: A: Minnesota; B: Iowa; C: Nebraska, D: North Carolina and E: Illinois

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”.

You can also listen to Dr. Arruda present some of these research findings at the 2017 Leman conference.


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.

NHF: PRRS is also a summer disease

Our latest collaboration with the National Hog Farmer was written by the Morrison Swine Health Monitoring Program team regarding the incidence of PRRS in the summer.

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.

Science Page: Incidence Year 2017/2018 Annual Summary

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.

Science Page: Update on EWMA all versus EWMA original 13

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,  we are sharing an update on the EWMA comparison by the MSHMP team.

Key points:

  • 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.


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.

Last year comparison of the EMWA

Production Losses From an Endemic Animal Disease: PRRS in Selected Midwest US Sow Farms

In this publication in Frontiers in Veterinary Science, Drs. Valdes-Donoso from UC Davis and Andres Perez from the Center of Animal Health and Food Safety (CAHFS) at the University of Minnesota, measured the impact of Porcine Reproductive and Respiratory Syndrome (PRRS) on the production of weaned pigs.

To do so, they monitored 16 different sow farms, all parts of a single production system in the Midwest for 48 weeks and recorded a total of 8 indicators:

  • number of weaned pigs
  • number of stillbirths per litter
  • number of live births per litter
  • number of pre-weaned dead
  • number of sows farrowing
  • number of sows repeating service
  • number of sows aborting
  • number of sows dead

For each farm and each indicator, the 12 weeks before the outbreak served as a baseline for the farm performances and the data was recorded until 35 weeks post outbreaks. All of the outbreaks occurred during the second half of 2014. The inventory of the farms varied between 2,714 and 6,009 breeding females.

The following figure represented the weekly average for the 8 recorded parameters from 12 weeks pre-outbreak to 35-weeks post-outbreak.

Perez PRRS sow farm losses Midwest

Based on these results, it was estimated that a PRRS outbreak caused a 7.4% decrease in weaned pigs per sow year, i.e., 1.92 fewer weaned pigs per breeding unit. In an average sized farm of this firm, the slight reduction in farrowing yielded a decline of 249 fewer farrows per year. The chances that a sow repeats service increased by 37%, while aborted fetuses increased by 26% in a year with a PRRS outbreak.

The primary estimate (using 12 weeks as pre-outbreak period) is that PRRS reduced weaned pig production per farm by 7.4% on an annual basis, leading to a decrease in output value per sow year of $86.6, or $367,521 per farm year for an average sized farm. If instead we assume the outbreak began in t −1 (i.e., using 11 weeks as pre-outbreak period), the estimated reduction in weaned pig production was 7.6%, or $88.8 less per sow year and an average revenue loss of $376,773 among the farms studied.

Results showed that weaned pig production declined in week − 1, although statistically insignificant, as did several performance indicators. The data suggest that the average PRRS outbreak in this set of farms began at least one week before it was announced.”

The rise in abortions was the strongest signal of PRRSV activity in our data. Increased surveillance, particularly to rising abortions, may allow farms to identify PRRS more quickly.

The length of PRRS outbreaks, as well as their effects over time, is highly variable. The results of this study demonstrate that PRRS has a negative effect on weaned pig production for a longer time than previously estimated. Indeed, the estimated means of weaned pig production remained below the baseline throughout the 35 weeks that we are able to observe following the outbreak.

For more details, read the open-access publication on the Frontiers in Veterinary Science website.


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.

Science Page: Effective disease surveillance and response strategies depend on detailed swine shipment data

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,  we are sharing a report regarding the use of swine shipment data for effective disease surveillance by Drs. Amy Kinsley, Meggan Craft, Andres Perez, and Kim VanderWaal.

Key point:

  • A production system’s vulnerability to disease spread can be greatly reduced when selectively identifying a subset of farms as disease control targets.

What was done:

In this study, we used a network approach to describe annual movement patterns between swine farms in three multi-site production systems (1,063 farms) in the United States.

We measured:

  1. degree: number of farms to which a farm ships or receives pigs
  2. farm’s individual contribution to disease spread via its movements
  3. mean infection potential (MIP), which measures potential incoming and outgoing infection chains

What was found:

Removing farms based on their mean infection potential substantially reduced the potential for transmission of an infectious pathogen through the network when compared to removing farms at random, as shown by a reduction in the magnitude of R0 attributable to contact pattern.
The MIP was more efficient at identifying targets for disease control compared to degree and farm’s contribution to disease spread.

What does this mean?

By targeting disease interventions towards farms based on their mean infection potential, we can substantially reduce the potential for transmission of an infectious pathogen in the contact network, and performed consistently well across production systems.
Fine-scale temporal movement data is important and is necessary for in-depth understanding of the contact structure in developing more efficient disease