Science page: Evaluation of biosecurity measures to prevent indirect transmission of 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 objective of the study presented today was to evaluate the efficacy of of biosecurity procedures directed at minimizing transmission via personnel following different protocols in controlled experimental settings.

Four (4) groups were housed in different rooms:

  • INF: Pigs infected with PEDV
  • LB: Naive pigs which were exposed to personnel coming from the INF room without changing PPE at all
  • MB: Naive pigs which were exposed to personnel coming from the INF room after washing their hands and face as well as changing footwear and clothing.
  • HB: Naive pigs which were exposed to personnel coming from the INF room after showering as well as changing clothing and footwear.

Results are shown in the figure below. Naive pigs were exposed to personnel from 44h after the pigs in the INF group were infected with PEDV until 10 days post infection.

PEDV indirect transmission biosecurity measures
Viral shedding of pigs. Movements were terminated at 10 dpi. Data presented are average values of viral RNA copies (± SD) of infected (INF), low biosecurity (LB), medium biosecurity (MB) and high biosecurity (HB) groups

Key points:

  • PEDV transmission is likely to occur with contaminated fomites in low biosecurity scenarios.
  • Indirect contact transmission of PEDV can happen very rapidly. Transmission was detected 24h after personnel moved from infected to low biosecurity rooms (no change in clothes, boots or washing hands)
  • Changing PPE (personal protective equipment) and washing skin exposed areas is beneficial to decrease the risk of PEDV transmission.

 

Link to the facilities diagram explaining the experiment setup as well as the results on PEDV indirect transmission in this study.

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

Abstract

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

Abstract

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

Science Page: Mycoplasma hyopneumoniae detection in nylon flocked and rayon bud swabs

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.

Sterile swabs are used to collect clinical samples from the pig’s respiratory tract. Research studies have shown that the sensitivity of respiratory pathogens detection can vary depending on the type of swab used for sample collection.

The objective of this study was to compare two types of commercial swabs for M. hyopneumoniae detection by real-time PCR.

nylon versus rayon swabs mycoplasma hyopneumoniae 2017.gif
Mycoplasma hyopneumoniae detection by real-time PCR. Results shown are Ct values.

Keypoints:

  • Absorption and detection of M. hyopneumoniae in nylon flocked swabs was significantly higher than rayon bud swabs.
  • Nylon flocked swabs could be suggested to use in chronic infections where the bacterial load could be low.

See the full report for more information on the absorption levels of the two different types of swabs.

Several influenza A genotypes detected in the same farm, sub-population, and pig

In this collaborative open-access research article from the University of Minnesota, five commercial sow farms were sampled regularly over a year. Sows, gilts, and piglets was sampled with nasal swabs. A little less than 5% of the samples were PCR positive for influenza A. The strains were classified in 7 groups based on their hemagglutinin (a surface protein of the virus) sequences. One additional group was created based on another gene segment.

Complete genome sequencing influenza A Diaz 2017

Several viral groups were detected in the sub-populations of all of the 5 farms, as shown in the figure below. Influenza strains combined segments from several viral groups were detected in three farms. Additionally, several strains were detected in individual animals showing the potential for reassortment and creation of new influenza strains.

Complete genome sequencing influenza A Diaz 2017 group
Influenza viral groups (VG) detected in each farm sub-populations over time (PG:piglets, GL: gilts, NG: new gilts)   *: month during which sampling started.

Abstract

Influenza A viruses (IAVs) are endemic in swine and represent a public health risk. However, there is limited information on the genetic diversity of swine IAVs within farrow-to-wean farms, which is where most pigs are born. In this longitudinal study, we sampled 5 farrow-to-wean farms during a year and collected 4,190 individual nasal swabs from three distinct pig subpopulations. 207 (4.9%) samples tested PCR positive for IAV, and 124 IAVs were isolated. We sequenced the complete genome of 123 IAV isolates, and found 31 H1N1, 26 H1N2, 63 H3N2 and 3 mixed IAVs. Based on the IAV hemagglutinin seven different influenza A viral groups (VGs) were identified. Most of the remaining IAV gene segments allowed us to differentiate the same VGs although an additional viral group was identified for gene segment 3 (PA). Moreover, the co-detection of more than one IAV VG was documented at different levels (farm, subpopulation, and individual pigs) highlighting the environment for potential IAV reassortment. Additionally, three out of 5 farms contained IAV isolates (n=5) with gene segments from more than one VG, and 79% of all IAVs sequenced contained a signature mutation (S31N) in the matrix gene that has been associated with resistance to the antiviral amantadine. Within farms, some IAVs were only detected once while others were detected for 283 days. Our results illustrate the maintenance and subsidence of different IAVs within swine farrow-to-wean farms over time, demonstrating that pig subpopulation dynamics is important to better understand the diversity and epidemiology of swine IAVs.

IMPORTANCE At the global scale swine are one of the main reservoir species for influenza A viruses (IAVs), and play a key role on the transmission of IAVs between species. Additionally, the 2009 IAV pandemics highlighted the role of pigs in the emergence of IAVs with pandemic potential. However, limited information is available regarding the diversity and distribution of swine IAVs in farrow-to-wean farms where novel IAVs can emerge. In this study we studied 5 swine farrow-to-wean farms during a year and characterized the genetic diversity of IAVs among three different pig subpopulations commonly housed in this type of farms. Using next generation sequencing technologies, we demonstrated the complex distribution and diversity of IAVs among the pig subpopulations studied. Our results demonstrated the dynamic evolution of IAVs within farrow-to-wean farms, which is crucial to improve health interventions to reduce the risk of transmission between pigs and from pigs to people.

Link to the full open-access article

Science Page: Antibiotic susceptibility in Pasturella multocida and Streptococcus suis isolated at the Minnesota VDL

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.

Antimicrobial resistance has been a preoccupying topic for the past few years. We talked before about what the definition of antibiotic resistance is and how it can be interpreted in two different manners. This week, Dr. Alvarez from the STEMMA lab is reporting the trends in antimicrobial susceptibility observed in strains of Streptococcus suis and Pasteurella multocida isolated at the Minnesota Veterinary Diagnostic Laboratory over the past 10 years. S. suis and P. multocida are common swine pathogens that can cause severe economic losses. Knowing which antibiotics are more likely to be efficient against those bacteria can help in tackling the disease faster.

Key Points:

  • MN-VDL data was analyzed to study antibiotic susceptibility in clinical isolates of Pasteurella multocida and Streptococcus suis from 2006 to 2016.
  • Isolates were highly susceptible to Ampicillin, Ceftiofur, Enrofloxacin and Florfenicol throughout the study period.
  • There were no changes in antibiotic susceptibility against the antibiotics tested routinely across the study period.

The full report can be read here.