This new publication in the Porcine Health Management journal is the result of a collaboration between the University of Barcelona in Spain, PIC (Pig improvement Company) and the MycoLab at the University of Minnesota.
321 farms were surveyed across Europe and Russia regarding their practices for gilt acclimation especially in the context of Mycoplasma hyopneumoniae. The farms are spread over 18 countries and this is reflected in the strong variation of the measures taken to acclimate the incoming gilt population.
Among the questions asked, the type of farm as well as the size of the herd were recorded. Regarding the gilts, the researchers took into account receiving schedule as well as origin and age in addition to the acclimation measures.
In the table below, you can see the summary of the measures taken to acclimate the gilts to Mycoplasma hyopneumoniae. The vast majority of the herds (77%) used vaccination either as a single intervention or coupled with exposure to sows about to be culled. Another popular option (22.4%) was no intervention at all.
Number of farms (%)according to the methods used for replacement gilt acclimation in terms of M. hyopneumoniae
Click on the table above to see the full open-access publication.
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 objective of the study presented today was to evaluate the efficacy of of biosecurity procedures directed at minimizing transmission via personnel following different protocols in controlled experimental settings.
Four (4) groups were housed in different rooms:
INF: Pigs infected with PEDV
LB: Naive pigs which were exposed to personnel coming from the INF room without changing PPE at all
MB: Naive pigs which were exposed to personnel coming from the INF room after washing their hands and face as well as changing footwear and clothing.
HB: Naive pigs which were exposed to personnel coming from the INF room after showering as well as changing clothing and footwear.
Results are shown in the figure below. Naive pigs were exposed to personnel from 44h after the pigs in the INF group were infected with PEDV until 10 days post infection.
Viral shedding of pigs. Movements were terminated at 10 dpi. Data presented are average values of viral RNA copies (± SD) of infected (INF), low biosecurity (LB), medium biosecurity (MB) and high biosecurity (HB) groups
Key points:
PEDV transmission is likely to occur with contaminated fomites in low biosecurity scenarios.
Indirect contact transmission of PEDV can happen very rapidly. Transmission was detected 24h after personnel moved from infected to low biosecurity rooms (no change in clothes, boots or washing hands)
Changing PPE (personal protective equipment) and washing skin exposed areas is beneficial to decrease the risk of PEDV transmission.
Bioaerosol sampling refers to the methods by which one is able to collect the particles of biological origin (microbial, animal, or plant) in the air. This is useful information in swine production because many economically important pathogens can be transmitted by air from one farm to the next. 73 scientific reports were included in this review published in the journal Frontiers in Veterinary Science. The information regarding the presence of viruses in the air around swine settings is limited but their findings has been compiled in the figure below. Overall, bioaerosol sampling could be a promising way to conduct non-invasive viral surveillance among swine farms.
Influenza A, PRRSV, PEDV detection downwind from farms with infected source populations
Abstract
Modern swine production facilities typically house dense populations of pigs and may harbor a variety of potentially zoonotic viruses that can pass from one pig generation to another and periodically infect human caretakers. Bioaerosol sampling is a common technique that has been used to conduct microbial risk assessments in swine production, and other similar settings, for a number of years. However, much of this work seems to have been focused on the detection of non-viral microbial agents (i.e., bacteria, fungi, endotoxins, etc.), and efforts to detect viral aerosols in pig farms seem sparse. Data generated by such studies would be particularly useful for assessments of virus transmission and ecology. Here, we summarize the results of a literature review conducted to identify published articles related to bioaerosol generation and detection within swine production facilities, with a focus on airborne viruses. We identified 73 scientific reports, published between 1991 and 2017, which were included in this review. Of these, 19 (26.7%) used sampling methodology for the detection of viruses. Our findings show that bioaerosol sampling methodologies in swine production settings have predominately focused on the detection of bacteria and fungi, with no apparent standardization between different approaches. Information, specifically regarding virus aerosol burden in swine production settings, appears to be limited. However, the number of viral aerosol studies has markedly increased in the past 5 years. With the advent of new sampling technologies and improved diagnostics, viral bioaerosol sampling could be a promising way to conduct non-invasive viral surveillance among swine farms.
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 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.
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 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.
