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

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

Abstract:

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:

MSHMP PEDV chart
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.

Science Page: Detecting influenza virus with a portable device

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.

We are presenting today the work done in Dr. Cheeran’s lab on the detection of influenza virus in farms. The objective of their research project is to develop a portable diagnostic platform that is capable of performing on-site testing of influenza viruses in swine with minimum sample handling and laboratory skill requirements.

The device is using giant magnetoresistance (GMR) technology. In a nutshell, if influenza viruses are present in the sample, they will bind to sensors, cause a disruption in resistance, and create an electric signal in the device that will be able to wirelessly transmit the result to a smartphone or computer.

Key points from this week edition:

  • Portable, hand held device for detection of influenza A virus (IAV) based on giant magnetoresistance (GMR) biosensor has been developed.
  • Although in its developmental stage, if successful this test has the potential for rapid on-site testing of influenza viruses in swine.

The first sensitivity tests of the device look very promising!

Science Page: Incidence risk and incidence rate

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’s Science page is a follow-up from the one presented last week and focuses on the difference between incidence rate and incidence risk. Those two epidemiological measurements are often mistaken for one another.

Key points from this week edition:

  • Incidence risk is a measure of disease occurrence over a defined period of time. It is a proportion, therefore takes values from 0 to 1 (0% to 100%).
  • Incidence rate takes into account the time an individual is at risk of disease. It is not a proportion since it defines the number of cases per animal or farm time at risk.
  • Incidence risk and Incidence rate are often confused. Incidence risk and rate are numerically the same when the period at risk does not vary across individuals being studied.

Take a look at the complete report to see an example of the difference between incidence risk and incidence rate on farms.

 

Science Page: PRRS cumulative incidence by status

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.

How does PRRS incidence vary based on farm status? This is the question answered in this week’s edition of the Science Page. Three different formulas were used to calculate the incidence in each of the group over type. First, the initial number of farms of each status at the beginning of the year was used as the denominator. Then, the denominator was changed to the total number of farms that entered each status since the beginning of the year. Lastly, weekly incidences calculated for each of the group since the beginning of the year were added. Calculations went back for the last 3 years.

Key points from this week edition:

  • Cumulative incidence is higher in those farms that are under status 2, 2vx and 2fvi.
  • The incidence is lower in farms that had recently an outbreak or those that are completely negative.
  • Different ways of calculating incidence by herd status lead to the same overall conclusion.

Take a look at PRRS incidences in farms of group 2 status, vaccinated or inoculated with live virus over the past years.