A constellation of strains co-circulate in pigs during influenza epidemics

This recent publication in Nature comes from the Torremorell’s lab and aims at answering the question of the number of strains circulating in pigs during an influenza outbreak and how genetically different they may be. The full article is available in open access, click on the banner below to access it.

Constellation influenza banner Torremorell

To answer the question of multiple strains of influenza in pigs, the group followed a cohort of 132 pigs placed in a 2,200-head a wean-to-finish barn, endemic for influenza. All the pigs originated from the same sow farm . The history of past influenza episodes did not include any information regarding the strain of viruses circulating in the barn. Nasal swabs were collected for each individual pig and were tested in the laboratory by PCR.

Results from this study showed that:

  • Only 2 pigs out of 132 tested negative every week during the entire duration of the study.
  • Around 88% of the pigs tested positive for influenza more than once.
  • 20.5% of pigs were positive for influenza at weaning.
  • Weekly influenza prevalence ranged between 0% and 65%.
  • 3 different viral groups were identified VG1, VG2, and VG3.
  • Groups belonged to the swine H1-gamma, H1-beta and H3-cluster-IV influenza A respectively. (Here is a review of the H1 genetic clades and one of the H3 genotype patterns)

The figure below shows the genetic make up of the influenza strains isolated each week, the viral group each genetic segment belonged to and the number of times this specific combination was found.

For example, the second line can be interpreted as: during week one, one sample in which 10 sequences were recovered, had influenza virus with segments 1, 2, 3, 4, 5, and 7 belonging to the Viral Group 1 (H1 gamma) and segments 6 and 8 were from Viral groups 1 and 3.

Influenza constellation Torremorell

In conclusion, this study shows that influenza infections in pigs after weaning and under field conditions are complex. The influenza virus genome is diverse and changes rapidly. Prolonged persistence of influenza viruses in pigs could be the result of multiple influenza epidemic events that take place repeatedly over time or the re-infection with influenza viruses that are closely related to each other.


Swine play a key role in the ecology and transmission of influenza A viruses (IAVs) between species. However, the epidemiology and diversity of swine IAVs is not completely understood. In this cohort study, we sampled on a weekly basis 132 3-week old pigs for 15 weeks. We found two overlapping epidemic events of infection in which most pigs (98.4%) tested PCR positive for IAVs. The prevalence rate of infection ranged between 0 and 86% per week and the incidence density ranged between 0 and 71 cases per 100 pigs-week. Three distinct influenza viral groups (VGs) replicating as a “swarm” of viruses were identified (swine H1-gamma, H1-beta, and H3-cluster-IV IAVs) and co-circulated at different proportions over time suggesting differential allele fitness. Furthermore, using deep genome sequencing 13 distinct viral genome constellations were differentiated. Moreover, 78% of the pigs had recurrent infections with IAVs closely related to each other or IAVs clearly distinct. Our results demonstrated the molecular complexity of swine IAVs during natural infection of pigs in which novel strains of IAVs with zoonotic and pandemic potential can emerge. These are key findings to design better health interventions to reduce the transmission of swine IAVs and minimize the public health risk.

Senecavirus A is still with us!

We are continuing our series on Senecavirus A this week with the latest paper written in our rubric for the National Hog Farmer.

Senecavirus is still with us NHF sept 17

More than 230 Senecavirus outbreaks have been confirmed after July 2015 in the United States and this is why it is important:

“The clinical signs in pigs infected with vesicular disease caused by SVA are variable and can range from no outward signs, to nonspecific signs such as decreased appetite or fever, or pigs may develop vesicles, or blisters, on the skin or in the mouth.[..]

While SVA continues to plague U.S. and global pork producers, it is important to be reminded of and understand some basic characteristics and behavior of this virus. SVA causes vesicular lesions affecting the skin, mouth and feet of pigs of all ages and has been associated with increased neonatal mortality which may be accompanied by neonatal diarrhea. If vesicular disease is present, your state animal health official must be notified in order to rule out other foreign animal diseases, such as FMD. The virus can be detected in multiple sample types but there is variability in the amount of time for which each sample type can be used for detection. Finally, SVA is extremely stable and contaminated facilities, transport vehicles and fomites are concerns for possible virus transmission but several disinfectants have been shown to be effective at neutralizing the virus.”

Bioaerosol sampling for airborne virus surveillance in swine facilities

Bioaerosol sampling refers to the methods by which one is able to collect the particles of biological origin (microbial, animal, or plant) in the air. This is useful information in swine production because many economically important pathogens can be transmitted by air from one farm to the next. 73 scientific reports were included in this review published in the journal Frontiers in Veterinary Science. The information regarding the presence of viruses in the air around swine settings is limited but their findings has been compiled in the figure below. Overall, bioaerosol sampling could be a promising way to conduct non-invasive viral surveillance among swine farms.

Viruses detected in radisuses around farms
Influenza A, PRRSV, PEDV detection downwind from farms with infected source populations


Modern swine production facilities typically house dense populations of pigs and may harbor a variety of potentially zoonotic viruses that can pass from one pig generation to another and periodically infect human caretakers. Bioaerosol sampling is a common technique that has been used to conduct microbial risk assessments in swine production, and other similar settings, for a number of years. However, much of this work seems to have been focused on the detection of non-viral microbial agents (i.e., bacteria, fungi, endotoxins, etc.), and efforts to detect viral aerosols in pig farms seem sparse. Data generated by such studies would be particularly useful for assessments of virus transmission and ecology. Here, we summarize the results of a literature review conducted to identify published articles related to bioaerosol generation and detection within swine production facilities, with a focus on airborne viruses. We identified 73 scientific reports, published between 1991 and 2017, which were included in this review. Of these, 19 (26.7%) used sampling methodology for the detection of viruses. Our findings show that bioaerosol sampling methodologies in swine production settings have predominately focused on the detection of bacteria and fungi, with no apparent standardization between different approaches. Information, specifically regarding virus aerosol burden in swine production settings, appears to be limited. However, the number of viral aerosol studies has markedly increased in the past 5 years. With the advent of new sampling technologies and improved diagnostics, viral bioaerosol sampling could be a promising way to conduct non-invasive viral surveillance among swine farms.

Link to the full article

Science Page: Analyzing swine movement patterns in relation with PEDV

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.

The movement of live pigs between farms is an important mechanism for disease introduction and spread. Thus, understanding the structure of livestock contacts and studying the routes, volumes, frequency, and the risks associated with animal movement is a prerequisite for effective disease surveillance and control in animal populations.
At the same time, local area spread between neighboring farms is also implicated in the spread of viruses such as porcine epidemic diarrhea virus (PEDV) and porcine reproductive and respiratory syndrome virus (PRRSV).

Even after controlling for hog density and season of the year, we showed that the number of pigs received into neighboring farms was an important predictor of PED infection risk.

relative importance of parameters in predicting PEDV
Relative importance in predicting risk, according to the Gini index

Key Points

  • Between farm transmission research in swine has primarily come from small studies rather than large scale datasets.
  • By looking at environmental/landscape, pig movements, and spatial factors, we studied the likelihood of a farm contracting PED from it’s neighbors.
  • It was found that the number of pigs received by neighboring farms was an important predictor of PEDV infection risk.

Click here to see the full Science page report on the various parameters and their relative importance in predicting risk of PEDV infection.

Translating big data into smart data for veterinary epidemiology: the MSHMP perspective

Big data can be defined as the daunting accumulation of abundant and diverse information. While recording data is the first step to measure progress or quickly identify an issue, the large amount of information collected can make it difficult to analyze.

At the University of Minnesota, one of the main projects using big data is the Morrison’s Swine Health Monitoring Program. This ongoing project collects veterinary reports and diagnostic results from numerous swine producers on a daily basis. The compiled information is then analyzed, interpreted and reported back as smart data to the participants every week. Smart data is commonly defined as a piece of information useful enough to make educated decisions.


Data pipeline utilized by the Morrison Swine Health Monitoring Project.jpg
Data pipeline used by the Morrison Swine Health Monitoring project for generating near real-time insight about the incidence of PRRSV


The increasing availability and complexity of data has led to new opportunities and challenges in veterinary epidemiology around how to translate abundant, diverse, and rapidly growing “big” data into meaningful insights for animal health. Big data analytics are used to understand health risks and minimize the impact of adverse animal health issues through identifying high-risk populations, combining data or processes acting at multiple scales through epidemiological modeling approaches, and harnessing high velocity data to monitor animal health trends and detect emerging health threats. The advent of big data requires the incorporation of new skills into veterinary epidemiology training, including, for example, machine learning and coding, to prepare a new generation of scientists and practitioners to engage with big data. Establishing pipelines to analyze big data in near real-time is the next step for progressing from simply having “big data” to create “smart data,” with the objective of improving understanding of health risks, effectiveness of management and policy decisions, and ultimately preventing or at least minimizing the impact of adverse animal health issues.

Review the full article

Infection dynamics and genetic variability of Mycoplasma hyopneumoniae in self-replacement gilts

This is a new research paper from the MycoLab under Dr. Maria Pieters’ supervision. In this study, the group looked at the infection dynamics and genetic variability of Mycoplasma hyopneumoniae in self-replacement gilts, in 3 positive herds. Serum samples were taken from the gilts at 150 days of age onward and laryngeal swabs were collected from the gilts and their progeny.

Highlights of this project

  • Genetic variability of M. hyopneumoniae was evaluated using MLVA typing.
  • The highest M. hyopneumoniae prevalence in gilts was detected at 150 days of age.
  • Detection patterns for M.hyopneumoniae were different among farms.
  • Genetic variability was identified within and among farms.


Pieters 2017 infection dynamics Mhyop


The aim of this study was to assess the longitudinal pattern of M. hyopneumoniae detection in self-replacement gilts at various farms and to characterize the genetic diversity among samples. A total of 298 gilts from three M. hyopneumoniae positive farms were selected at 150 days of age (doa). Gilts were tested for M. hyopneumoniae antibodies by ELISA, once in serum at 150 doa and for M. hyopneumoniae detection in laryngeal swabs by real time PCR two or three times. Also, 425 piglets were tested for M. hyopneumoniae detection in laryngeal swabs. A total of 103 samples were characterized by Multiple Locus Variable-number tandem repeats Analysis. Multiple comparison tests were performed and adjusted using Bonferroni correction to compare prevalence of positive gilts by ELISA and real time PCR. Moderate to high prevalence of M. hyopneumoniae in gilts was detected at 150 doa, which decreased over time, and different detection patterns were observed among farms. Dam-to-piglet transmission of M. hyopneumoniae was not detected. The characterization of M. hyopneumoniae showed 17 different variants in all farms, with two identical variants detected in two of the farms. ELISA testing showed high prevalence of seropositive gilts at 150 doa in all farms. Results of this study showed that circulation of M. hyopneumoniae in self-replacement gilts varied among farms, even under similar production and management conditions. In addition, the molecular variability of M. hyopneumoniae detected within farms suggests that in cases of minimal replacement gilt introduction bacterial diversity maybe farm specific.

Access to the full version of the paper

Science Page: Are the farms that broke with PED the same?

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 proud to introduce a new chart in the Morrison’s Swine Health Monitoring Project. This new addition will be able to answer a common question regarding PEDV outbreaks:

Are the farms currently breaking with PEDV the same than the ones which broke in the past?

To interpret the figure, follows these steps.

  • Horizontal axis represents all the farms that borke with PEDV during the season 2016/2017, with each tick representing an individual farm
  • Vertical axis shows the previous seasons with 2016-2017 on top and 2012-2013 at the very bottom.
  • The color of the cell (year : farm) represents the number of outbreaks experienced; darker blue meaning more outbreaks.

Here is the example of this chart presented this week:

Outbreak history of farms that broke during the 2016-2017 season.

Key points:

The farms that break with PEDV do not appear to have a history of PEDV infections in the prior season.

Of the farms that broke during the 2016/17 season, only 5 (6.5%) of them had outbreaks during the previous season and 43 (56.6%) of them had broken at some point since 2013.

Only one farm has had an outbreak every year since the beginning of the epidemic in the US (season 2013/14).

The full report is available.