In today’s post, we would like to highlight the value of flu surveillance in swine as well as to acknowledge the University of Minnesota Veterinary Diagnostic Laboratory (UMN-VDL) as a long-standing and committed contributor to the USDA Voluntary Influenza A Virus (IAV) in Swine Surveillance program. Thanks to this surveillance program, the U.S. swine industry has ample information available for analysis and to support influenza-related research, vaccinology and diagnostics.
In this new scientific publication from Dr. Jorge Garrido, PhD candidate from the Torremorell lab, numerous sampling strategies to monitor influenza were compared. the following individual, litter, and environmental samples were included in the study:
In this second episode, Dr. Montserrat Torremorell, Dr. Adam Schelkopf (Pipestone Veterinary Services), Dr. Gordon Spronk (Pipestone Veterinary Services), and Dr. Tom Wetzell (Boehringer Ingelheim) continue the conversation on the challenges of IAV-S in day to day operation, the approaches to identifying infected pigs, and the processes that need to be put in place to reduce infection and increase survivability of pigs.
Podcasts are perfect for summer! We are presenting you with a new series on swine influenza from “At The Meeting… Honoring Dr. Bob Morrison in collaboration with SwineCast.
In this first episode, Dr. Montserrat Torremorell (University of Minnesota), Dr. Marie Culhane (University of Minnesota), Dr. Gordon Spronk (Pipestone Veterinary Services), and Dr. Tom Wetzell (Boehringer Ingelheim), talk about the issues of influenza in humans and swine, the state of surveillance of influenza in pigs and humans, and the biosecurity needed to help prevent the spread of the influenza virus between human and pigs.
Porcine reproductive and respiratory syndrome virus (PRRSv) infection causes a devastating economic impact to the swine industry. Active surveillance is routinely conducted in many swine herds to demonstrate freedom from PRRSv infection. The design of efficient active surveillance sampling schemes is challenging because optimum surveillance strategies may differ depending on infection status, herd structure, management, or resources for conducting sampling. Here, we present an open web-based application, named ‘OptisampleTM’, designed to optimize herd sampling strategies to substantiate freedom of infection considering also costs of testing. In addition to herd size, expected prevalence, test sensitivity, and desired level of confidence, the model takes into account the presumed risk of pathogen introduction between samples, the structure of the herd, and the process to select the samples over time. We illustrate the functionality and capacity of ‘OptisampleTM’ through its application to active surveillance of PRRSv in hypothetical swine herds under disparate epidemiological situations. Diverse sampling schemes were simulated and compared for each herd to identify effective strategies at low costs. The model results show that to demonstrate freedom from disease, it is important to consider both the epidemiological situation of the herd and the sample selected. The approach illustrated here for PRRSv may be easily extended to other animal disease surveillance systems using the web-based application available at http://stemma.ahc.umn.edu/optisample.