North American swine rotaviruses: a complex epidemiology

A scientific paper published today in PLOS ONE reveals that based on three-level mixed-effects logistic regression models, the epidemiology of swine rotaviruses in North America is quite complex. The goal of the study led by Drs. Homwong, Perez, Rossow, and Marthaler from the University of Minnesota was to investigate the associations among age, rotavirus detection, and regions within the US swine production in samples submitted for diagnosis to the Minnesota Veterinary Diagnostic Laboratory.

journal.pone.0154734.g002

Percentages of Rotavirus A (RVA), Rotavirus B (RVB), and Rotavirus C (RVC) samples by state.
The color represented highest prevalence of the RV species (green represents RVA, purple represents RVB, blue represents RVC while pink represents equal percentages of RVA and RVC

Abstract: Rotaviruses (RV) are important causes of diarrhea in animals, especially in domestic animals. Of the 9 RV species, rotavirus A, B, and C (RVA, RVB, and RVC, respectively) had been established as important causes of diarrhea in pigs. The Minnesota Veterinary Diagnostic Laboratory receives swine stool samples from North America to determine the etiologic agents of disease. Between November 2009 and October 2011, 7,508 samples from pigs with diarrhea were submitted to determine if enteric pathogens, including RV, were present in the samples. All samples were tested for RVA, RVB, and RVC by real time RT-PCR. The majority of the samples (82%) were positive for RVA, RVB, and/or RVC. To better understand the risk factors associated with RV infections in swine diagnostic samples, three-level mixed-effects logistic regression models (3L-MLMs) were used to estimate associations among RV species, age, and geographical variability within the major swine production regions in North America. The conditional odds ratios (cORs) for RVA and RVB detection were lower for 1–3 day old pigs when compared to any other age group. However, the cOR of RVC detection in 1–3 day old pigs was significantly higher (p < 0.001) than pigs in the 4–20 days old and >55 day old age groups. Furthermore, pigs in the 21–55 day old age group had statistically higher cORs of RV co-detection compared to 1–3 day old pigs (p < 0.001). The 3L-MLMs indicated that RV status was more similar within states than among states or within each region. Our results indicated that 3L-MLMs are a powerful and adaptable tool to handle and analyze large-hierarchical datasets. In addition, our results indicated that, overall, swine RV epidemiology is complex, and RV species are associated with different age groups and vary by regions in North America.

Link to the full article

iCOMOS: One Medicine One Science

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The 2nd International Conference on One Medicine One Science will be held from April 24th to April 27th at the Commons Hotel in Minneapolis.

iCOMOS is a global forum to:

  • communicate the importance of science in solving pressing health issues at the interface of humans, animals and the environment;
  • facilitate interdisciplinary, international collaborations embracing health, science and economics;
  • inform public policy development that is necessary for preserving human and animal health.

Human and animal health care scientists and professionals, economists, trainees, environmental scientists, ethicists, public health and chronic disease specialists, and policy experts in health, agriculture, food, and environmental affairs are invited to come and exchange on this essential topic that is One Health.

Click here to see the full program.

 

UMN well represented at 2016 AASV

UMN students did a fantastic job at the 2016 American Association of Swine Veterinarians (AASV) meeting. Four students presented their projects as a poster presentation and two gave a presentation, reaching 2nd and 3rd position of the student competition. Alyssa Anderson was one of the five students awarded with the Merck-AASV Foundation scholarship.

On the faculty side, Dr. Mike Murtaugh’s research project concerning the development of  a challenge-free model to predict vaccine efficacy, was one of the four recognized by and received support from the AASV Foundation.

Congratulations to all!

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