Sample types and diagnostic methods for early detection of Mycoplasma hyopneumoniae

In lieu of the Science Page today, we are bringing you our most popular articles on the blog this past year: a publication by Dr. Maria Pieters, head of the MycoLab called Sample and diagnostic types for early detection of Mycoplasma hyopneumoniae.


Mycoplasma hyopneumoniae is the causative agent enzootic pneumonia, an economically significant disease in pigs. In this study published by Drs. Pieters and Rovira from the University of Minnesota, pigs experimentally inoculated with M.hyopneumoniae were sampled 0, 2, 5, 9, 14, 21, and 28 post-inoculation.

Different sample types were compared:

  • Nasal swabs
  • Laryngeal swabs
  • Tracheobronchal lavages
  • Oral fluids
  • Serum samples

Using different diagnostic tests:

  • PCR
  • ELISA IgG anti M.hyopneumoniae
  • ELISA Ig M anti M.hyopneumoniae
  • ELISA C-reactive protein

Laryngeal swab samples tested by PCR were highly sensitive for detection of Mycoplasma hyopneumoniae in live pigs. Various commercial ELISA kits for detection of Mycoplasma hyopneumoniae antibodies showed similar sensitivity. Oral fluids showed a low sensitivity for detection of Mycoplasma hyopneumoniae in experimentally infected pigs.

Link to the full-article

The emergence and evolution of influenza A (H1α) viruses in swine in Canada and the United States

Today, we are sharing a recent publication on swine influenza in the Journal of General Virology. Dr. Marie Culhane from the University of Minnesota collaborated on this study of the genetic diversity of swine viruses in Canada and how it influences the strains found in the US.

The final data set included:

  • 168 genomes from Canadian swine influenza A viruses,
  • 5 genomes from highly under-represented US states (Alabama, Arkansas, Kentucky, Maryland and Montana),
  • 648 genomes from US and Canadian swine influenza A viruses (GenBank).

In total, these data represented 29 US states and 5 Canadian provinces.

Genetic diversity of influenza A viruses

In Canada, H1α viruses were the most frequently identified H1 viruses. In contrast, H1α viruses died out long ago in US herds, and have only been identified sporadically following new viral introductions from Canada. Notably, the two dominant H1 viruses in the United States, H1γ and H1δ-1, were not observed in any Canadian province during 2009–2016. In contrast to H1, H3 viruses are found in both the United States and Canada, with evidence of frequent cross-border transmission.

Sources of viral diversity

The study shows that the source of influenza viruses is aligned with pig movements. Indeed, Iowa and Minnesota receive around 87% of Manitoba swine exports. Therefore, the patterns of swine influenza viruses in those 2 US states correlate with the ones in Manitoba.

Similarly, viral gene patterns found in Illinois, Michigan, Wisconsin, or Ohio are influenced by the ones found in Ontario. Indeed, it only takes 3 hours to transport pigs from Ontario to Michigan. However, North Carolina and Virginia are the largest source of viruses for this region.


Left: Each region is shaded according to the proportion of total ‘Markov jump’ counts from that particular region into the Heartland: red, high proportion of jumps, important source of viruses; light yellow, low proportion of jumps, not an important source of viruses; black, destination. Right: US states are shaded according to the number of live swine imported from Manitoba in 2015 (per 1000 head)


Swine are a key reservoir host for influenza A viruses (IAVs), with the potential to cause global pandemics in humans. Gaps in surveillance in many of the world’s largest swine populations impede our understanding of how novel viruses emerge and expand their spatial range in pigs. Although US swine are intensively sampled, little is known about IAV diversity in Canada’s population of ~12 million pigs. By sequencing 168 viruses from multiple regions of Canada, our study reveals that IAV diversity has been underestimated in Canadian pigs for many years. Critically, a new H1 clade has emerged in Canada (H1α-3), with a two-amino acid deletion at H1 positions 146–147, that experienced rapid growth in Manitoba’s swine herds during 2014–2015. H1α-3 viruses also exhibit a higher capacity to invade US swine herds, resulting in multiple recent introductions of the virus into the US Heartland following large-scale movements of pigs in this direction. From the Heartland, H1α-3 viruses have disseminated onward to both the east and west coasts of the United States, and may become established in Appalachia. These findings demonstrate how long-distance trading of live pigs facilitates the spread of IAVs, increasing viral genetic diversity and complicating pathogen control. The proliferation of novel H1α-3 viruses also highlights the need for expanded surveillance in a Canadian swine population that has long been overlooked, and may have implications for vaccine design.

Best of Leman 2017 series #3: J. Lowe – Understanding cull sow movements in North America

We launched a new series on the blog in October. Once a month, we are sharing 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.

Our third presented is from Dr. Jim Lowe from the University of Illinois on the movements of cull sows in North America and what it implies in terms of disease transmission.

To listen to this talk, please click on the picture below.


Happy holidays to you and your loved ones!

Science page: Investigating PRRS summer outbreaks in the US

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 investigation into PRRS summer outbreaks by Dr. Juan Sanhueza and the MSHMP team.

Key Points

  • Each year approximately 3% of the sow farms have a PRRS outbreak during the summer.
  • The incidence of summer PRRS breaks has been constant over the last 9 years.
  • There are geographical areas with higher or lower risk of summer breaks.
prevalence of summer PRRS outbreaks per year
Figure 1. PRRS summer outbreak incidence per year between 2009 and 2017.

A summer outbreak was defined as a PRRS case that happened between June 21st and September 21st of the year. The mean incidence of PRRS summer outbreaks was 3.2% between 2009 and 2017, ranging between 1.6% and 4.4%. The trend was stable among the years. (Figure 1) Not all areas are equal against summer outbreaks. Indeed, the region of Southern Minnesota – Northern Iowa is more at risk of outbreaks than others like Southern Iowa or Eastern North Carolina. (Figure 2)

areas with higer lower PRRS risk in the summer
Figure 2. Geographical areas with higher (red) and lower (blue) PRRSV incidence risk

Biosecurity measures against PRRSV should therefore be a concern all year round for swine producers!


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.