Tag: Epidemiology

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.

PhD seminar: Epidemiological investigation of a non-reportable endemic disease: PRRS in the US

Title: Epidemiological investigation of a non-reportable endemic disease: Porcine reproductive and respiratory syndrome (PRRS) in the US

Presented by:   Pablo Valdes-Donoso

Date:    Friday, June 9, 2017
Time:    3:00 – 4:00 pm
Place:    385-J, AS/VM Building

Abstract: Porcine reproductive and respiratory syndrome (PRRS), caused by a highly mutagenic and resistant RNA-virus, is an endemic disease that has been noted as one of the most important animal production diseases in the US because of its large economic damage on the swine industry. Nonetheless, there is no official control framework for this disease, so producers rely on voluntary regional control programs (RCPs) for its mitigation. Despite efforts to control PRRS, it persists in the environment, affecting a large number of farms every year. Using information shared by a specific RCP (RCP-N212), this dissertation focused on important aspects of PRRS dynamics within an RCP. Specifically, this dissertation encompassed five chapters. An introductory chapter is followed by the second chapter, which quantifies the extent to which RCPs contribute to PRRS control. After that, a prediction of network structure was made to forecast animal movements among farms within the RCP-N212. Then, longitudinal data collected from sow farms were used to measure the impacts of PRRS on production. Finally, a disaggregated disease diffusion model was used to depict PRRS dynamics within the RCP-N212, as well as to evaluate individual and collective strategies adopted by producers. This dissertation provides insight to the evaluation of regional control strategies that may be used as a framework for a formal PRRS control program.