Science Page: PRRS eradication efforts in Chile: Current situation and future prospects

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 on PRRSV eradication efforts in Chile.

Key Points

  • After being introduced in 1999, PRRS was eradicated from the country in 2012.
  • In 2013 PRRS was again detected, sequence analysis suggested this was a new introduction to the country.
  • The Chilean swine industry and the Chilean Veterinary Services (SAG) expect to again eliminate the disease in the near future.

PRRS is a notifiable disease in Chile. It was first detected in 1999, and in 2000 both the swine industry and government joined efforts to eradicate the disease by a series of coordinated events including a mixture of herd closure and depopulation of infected premises. Vaccination was not allowed in the country to control PRRSV infection. The eradication program was completed in 2007 and as a result, Chile was declared PRRSV free in 2012. Nevertheless, on October 2013 clinical signs compatible with PRRSV were reported in a commercial sow farm. Since then, all commercial herds performed surveillance activities according to a risk score based on location and biosecurity measures. From October 2013 to October 2017, approximately 153,000 blood samples have been analyzed.

Chile eradication of PRRSVViral sequences obtained during the 2013 outbreak were compared to sequences from the early 2000s outbreak in Chile. Results showed a large genetic difference between isolates from both outbreaks. Further analyses demonstrated that the Chilean virus was closely related to a virus circulating in the state of Indiana in the US at the time of introduction. These results suggested that the latest PRRSV outbreak in Chile was most likely due to a new introduction into the country rather than a reemergence of a strain previously detected in Chile.

By October 2017, the disease was restricted to approximately 45,000 animals in six commercial farms owned by two companies that currently have eradication programs in place. These six infected commercial sites are clustered in three areas. (See figure above)

US PRRSv surveillance using risk mapping and species distribution modeling

Today, we are sharing a publication from the Preventive Veterinary Medicine journal, by Dr. Andres Perez and the STEMMA laboratory. The goal of the study was to quantify the combined effect of factors such as season and herd size on the spatial range of high-risk areas for PRRSV outbreaks. Using Species Distribution Model, the team extracted associations between hypothesized risk factors and disease occurrence.


  • A species distribution model was used, to predict the spatial risk of PRRSv in swine populations across the U.S.
  • All of the Maxent spatial models identified high-risk areas, with probabilities greater than 0.5.
  • Relative contribution of pig density to PRRSv risk was higher in densely pig populated areas.
  • Relative contribution of climate and land cover to PRRSv risk were important in areas with relative low pig densities.
  • Ecological dynamics of PRRSv are different between swine production region in the U.S.

The largest number of PRRSv outbreaks in the U.S., as reported in the MSHMP, was observed in north central parts of Iowa, followed by south central areas of Minnesota. However, our crude U.S. Maxent model identified eastern North Carolina, southern Minnesota, and northern Iowa as high-risk areas for PRRSv outbreaks. As expected, pig density accounted for most of the PRRSv spatial risk (81.3% relative contribution). Climate (interpreted as the percentage of day-to-night temperature oscillation compared with the summer-to-winter oscillation, and mean temperature of the warmest quarter) accounted for the remaining spatial risk. Overall, the crude Maxent model suggested geographical areas with high pig densities and with a low level of daily temperature variability to the year are mostly suitable for circulation and maintenance of PRRSv.

Factors percent contribution PRRS outbreak Perez 2017
Summary charts of the estimated relative percent contribution of each environmental and demographic variable of the final Maxent model for each swine production region in the U.S.

The model for the South East region indicated that pig density was the most important predictor; followed by precipitation of the wettest month, land cover, and temperature seasonality. The relative contribution of pig density was smaller for this region compared to the Midwest. Specifically, geographical locations with high pig density, precipitation amount between 120 and 200 mm during the wettest months, and that were located within croplands were mostly suitable for PRRSv outbreaks in North Carolina and Northern South Carolina.

Additionally, the spread of PRRSV under certain conditions was more evident for the regions where pig density is relatively low. For example, in Illinois and Indiana and Kansas, Colorado, Oklahoma and Texas, wet weather and temperatures above 0 °C were more important in predicting the spatial risk of PRRSv than pig density.

Click here to read the entire publication on US PRRSv surveillance using risk mapping and species distribution modeling.


PRedicting and mapping PRRSV outbreaks PEREZ 2017.jpg


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.

Science Page: Monitoring breeding herd production data to detect PRRSV outbreaks

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 from Drs. Gustavo Silva and Daniel Linhares and his team at Iowa State University.

Key points:

  • Systematic monitoring of key production performance indicators allowed for early detection of PRRS outbreaks.
  • Number of abortions was the most efficient parameter, detecting outbreaks up to 4 weeks before being reported to MSHMP.
  • Early detection of signals associated with disease outbreaks may help in preventing further spread of the virus to other herds, and allowing implementation of rapid response intervention(s).

Two-years worth of reproductive performance data from a production system with 14 breeding herds (1,512 herd weeks) was gathered. Weekly data on number of abortions, pre-weaning mortality (PWM) and difference between total born and born alive (neonatal losses), were merged with weekly MSHMP PRRSV status. A statistical process control method was used to scan production data for significant deviations from baseline.

Linhares EWMA application to detect significant deviation in abortions.gif
Example of EWMA application to detect significant deviation in abortions, compared to changes in PRRS status over time.

The time-to-detect outbreak, 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.

Abortion signals were detected 1 to 4 weeks before outbreaks were reported to the MSHMP. Most pre-weaning mortality signals coincided with the outbreak date reported to the MSHMP, and prenatal losses signals were detected from 1 to 3 weeks after the MSHMP reported outbreak date. Overall, the models had high relative sensitivity (range 85.7 to 100%) and specificity (range 98.5% to 99.6%) when comparing to the changes in
PRRS status reported in the MSHMP database.

Science Page: Why PRRS elimination doesn’t work in some herds

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 article by Dr. Amber Stricker from Suidae Health and Production, published in

“Over the years, there’s been considerable progress in the development of strategies aimed at eliminating porcine respiratory and reproductive syndrome virus (PRRSV). I define successful PRRSV elimination as the absence of clinical disease in the breeding herd and, more importantly, the absence of the vertical transmission of virus to weaned pigs. Unfortunately, successful PRRS elimination isn’t always achieved in some herds, and I have several experiences that may help answer why.”
Dr. Stricker then compiles six reasons that, in her experience, led to a failure in PRRS elimination:
  • No break in disease cycle or insufficient herd closure
  • Incomplete exposure
  • Compliance problems
  • Holding back pigs
  • New virus introduction
  • Insufficient diagnostics

Take a look at the full article to read more about each of those facors.

Best of 2017 Leman series #1: C. Vilalta – Novel sampling strategies for piglets

We are launching a new series on the blog today. Once a month, we will share with you a presentation given at the 2017 Allen D. Leman swine conference, on topics that the swine group found interesting, innovative or that lead to great discussions. If there is a presentation from this year’s conference that you would like to hear again, please fill out the form at the end of this note.

To launch this series, Dr. Carles Vilalta from the University of Minnesota shares novel PRRSV sampling strategies for piglets. To listen to his presentation, click on the image below.

Vilalta new PRRS sampling Leman 2017

To read more about processing fluids in PRRSV diagnostics, you may read this Science Page written on the same topic.

Science Page: Use of processing fluids for PRRSV diagnostics

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.

Key points

  • Using processing fluids as a diagnostic tool can help us to detect lower PRRS prevalence in the herd.
  • Testicles and tails should be collected in a pail as they are potential spreaders of PRRS in the farrowing room.
  • We should target young parity sows for PRRSV sampling.

Processing fluids PRRS table.gif

What are processing fluids?

In sow farms, piglets get processed during the first week of life. This means that their tails is docked and the males are castrated. The farmer usually collect tails and testicles in a pail to be discarded at a later time.

We propose to use the fluids accumulating at the bottom of the pail to assess the farm PRRSV status.

How did we test those fluids?

The fluids were tested for PRRSV by PCR and the results were compared to the gold standard for this diagnostic: PCR on serum. Sampling was set in a farm that just went through a PRRSV outbreak and 10 litters from various parity sows were selected each week for 8 weeks.

What were the results?

Processing fluids were efficient in detecting PRRSV even if there was only one piglet positive in the litter (determined with the serum samples). Compared to the serum tests, there were 4 false negative samples that were explained by the fact that the virus load in the piglets serums was low and the dilution effect of the processing fluids caused the samples to get negative results. We also found 4 false positive resutls that could be due to cross-contamination of the samples despite the extreme care with which the samples were handled.

Are processing fluids a worthwhile sample?

The agreement between processing fluids and serum results was good and the sensitivity and specificity of the technique was respectively of 83% and 92%. Additionally, this technique requires no further handling of the piglets or use of extra supplies to collect samples and submit them to the laboratory.


Science page: Are patterns of spatiotemporal clustering of PRRSv consistent across years?

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 studied a subset of MSHMP participants located in the Midwest to test if some location/time combinations are more prominent during certain seasons across the years. Data from 358 farms in 10 management systems from 2011 to 2015 was compiled to look for clusters.

The clusters found by the SaTScanTM software are represented below. The red circles represent clusters identified in the time period from January to June, whereas blue ones are July to December. We can note that clusters were identified every year but that they varied with time.

Significant PRRS spatial cluster midwest
Significant spatial clusters for PRRSV in the Midwest between 2011 and 2015.

Key points

  • PRRS cases are recognized to be seasonal and aggregated by geographical space.
  • However, spatiotemporal patterns of PRRS clustering were not consistent across years.
  • Drivers of infection spread may vary over the years.

Future uses for this model can be found in the entire report