The UMN CVM students did a fantastic job at the 2018 American Association of Swine Veterinarians (AASV) meeting last week. Zhen Yang presented an update on his research regarding PCV3 and got the second place in the student oral competition.
Taylor Homann received a prize for her poster presentation. Marjorie Schleper was awarded one of the 10 student scholarships given by Merck Animal Health. Hunter Baldry was recognized for the most downloaded podcast: her interview of Dr. Clayton Johnson.
Lastly, Dr. Juan Sanhueza received one award given by Boehringer Ingelheim to advance the research on swine respiratory pathogens for his project: “Evaluation of parity as a delaying factor to reach PRRSv stability in sow farms”. Dr. Perle Boyer received a research grant from the AASV Foundation to develop Day 1 competencies for swine veterinary graduates.
It is a public, private and academic partnership to implement a system for near real time global surveillance of swine diseases.
The output of the system is a report of hazards identified and subsequently scored that may represent a risk for the US pork industry.
Developing systems to provide situational awareness to stakeholders in near-real time can facilitate the coordination between government agencies and the industry with the ultimate objective of preventing or mitigating the impact of diseases epidemics.
The system of near real time global surveillance of swine diseases is based on an online application. Initially focused on three main potential
threats: Classical Swine Fever (CSF), African Swine Fever (ASF), and Foot and Mouth Disease (FMD), it will expand to other exotic swine diseases in the US in the near future. A report, distributed on a monthly basis by SHIC, includes a list of identified hazards that may represent a risk for the US.
Several steps are needed to build the Swine Global Surveillance report as shown in the figure above.
Screening/Filtering phase: Multiple official data sources and soft data sources are systematically screened to build a raw repository. After that, an Include/exclude process is undertaken under a crowdsourcing model.
Scoring phase: A multi-criteria rubric was built based on: credibility, scale and speed of the outbreak, connectedness, local capacity to respond and potential financial impact on the US market. Each event is score independently by a group of experts.
Quality assurance (QA)/building: Its aim being to ensure that the design, operation, and monitoring of processes/systems will comply with the principles of data integrity including control over intentional and unintentional changes to information. The monthly report is put into a PDF document automatically from the app after the scoring process is finalized. At last, assembly of figures and proofreading is done before sending it to SHIC for monthly publication.
Complete automation of event capture into the database
Expansion of the list of diseases in the report
Shrinking the gap between Search/Filter phase and Final Publication – (1 week)
Expanding scoring experts panel
Process documentation – Quality assurance compliance
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.
Influenza is endemic and seasonal in piglets from sow farms in the Midwest with higher infections in winter and spring.
Influenza seasonality was partially explained by outdoor air absolute humidity and temperature trends.
Influenza genetic diversity was high and co-circulation of more than one genetically distinct virus was common.
To study influenza levels over time and its seasonality, monthly testing data of piglets at weaning from 34 sow farms during ~5 years were analyzed.
There were 28% of positive submissions with a median influenza herd-level prevalence of 28%. Genetic diversity was significant with 10 genetically distinct clades of contemporary US swine influenza viruses as shown below. Furthermore, 21% of farms had 3 genetically distinct viruses circulating over time; 18% had 2, 41% had 1 and 20% had no isolates available.
In summary, influenza herd-level prevalence in Midwestern sow farms had a seasonal pattern with higher levels in winter and spring. This is important to better allocate influenza control strategies such as vaccination in sow farms. Influenza seasonality was partially explained by outdoor air absolute humidity and temperature although other factors such as immunity and new introductions may play a role in the observed seasonality.