The objectives of the study are to describe the occurrence of PRRSv in the filtered sow herd population within MSHMP and to assess the associations between farm-level factors and the introduction of PRRSv into filtered sow herds. The results of the study may guide practitioners and veterinarians to modify their management and biosecurity practices in filtered sow herds.
Who can enroll?
All filtered sow herds of MSHMP participants will be eligible for the study. The database will be used together with the PRRSv incidence measure to understand occurrence of PRRS before and after filters were installed. A survey has been created to collect farm specific data such as:
Date when herd was filtered
Type of ventilation (negative or positive)
Back draft prevention methodology
Type of pre-filter and filter
Pre-filter and filter replacement frequency
Number of barns and load outs
Frequency of gilt introduction and weaning events
If you are interested in participating, please contact Dr. Cesar Corzo at corzo(at)umn.edu
Strong evidence of area spread was not found after evaluating three farm clusters located in two swine dense regions.
All barns of a nursery/finishing site should be sampled to define status.
Sick pen might not be the best target when sampling for PRRSV in grower pig sites
Background and Objectives
Area spread refers to the transmission of a pathogen (here PRRSV) through small particles in the air as well as through fomites on which the pathogen would have deposited on.
The objective of the study was to determine if the virus detected in a recently infected sow farm was similar to the one detected in neighboring farms (in other words: was local spread a likely source of infection?)
Methods and Results
35 farms were monitored for PRRSV. As soon as a farm broke, all of the neighboring farms were sampled for PRRSV independently of the type of production on site. If a sick pen was present on the farm, effort was made to include it in the sampling. Positive samples were then sequenced to compare to the original virus from the outbreak.
For two of the three area spread assessments performed, no similar sequence to the one obtained from the farm under investigation was found. Also it was not always possible to detect PRRSV in sick pens of the growing pig sites sampled in our study.
The EWMA chart is a smoothed chart of the percentage of farms that are breaking.
Newly added farms to MSHMP increase the denominator therefore diluting the estimate which affects the EWMA chart giving the impression that PRRS season has changed.
Reminder: What is the EWMA?
The Exponential Weighted Moving Average (EMWA) is a statistical method that averages data over time, continually decreasing the weight of data as it moves further back in time. An EWMA chart is particularly good at monitoring processes that drift over time and is used to detect small shifts in a trend.
In our project, EWMA is used to follow the evolution of the % of farms at risk that broke with PRRSV every week. EWMA incorporates all the weekly percentages recorded since the beginning of the project and gives less and less weight to the results as they are more removed in time. Therefore, the % of farms at risk that broke with PRRSV last week will have much more influence on the EMWA than the % of farms at risk that broke with PRRSV during the same week last year.
MSHMP report chart 4 depicts: 1)the number of new cases (green dots – secondary Y axis) during a specific week and 2)the percentage of farms that broke during that week of the total in the MSHMP project in a smoothed way (blue line/Y axis). The red horizontal line indicates the threshold (upper confidence limit – UCL). This UCL is calculated based on the average of cases during the lowest PRRS months in the year, June, July and August and is recalculated every two years.
When there are more cases than expected, the blue line crosses the threshold (red line) indicating there is an epidemic.
The formula used in the EWMA chart is the following:
where E is the smoothed % of infected herds, lambda the constant smoothing the curve, I the % of infected herds during that week and Et-1 is the smoothed % of infected herds during the previous week.
If different smoothing factors are applied to the MSHMP data this would generate different trends and then we would place the threshold based on the sensitivity
that we consider that signals an epidemic.
Has the incidence of PRRS changed?
One possible reason the EWMA % of cases decreasing might be that the number of farms that are breaking expressed as a percentage is less. This can be due to the fact that the total number of farms sharing PRRS status has been increasing and these new farms might have a lower underlying incidence.
Abortion cases in the study had a high rate of PCV3 positivity.
PCV3 found in association with lesions in an abortion case suggesting causality.
The study looked at 730 cases from the UMN Veterinary Diagnostic Laboratory with a positive sample for PCV3, received between Feb 2016 and Jan 2018.
Out of 22 states, 18 states were PCV3 positive. PCV3 was detected in pigs from all ages.
The positive rate among fetus, piglets, nursery and finishing pigs ranged from 15% to 20%. The PCV3 rate in adults was 35%.
PCV3/PCV2 co-infection rate was 5.2%, and PCV3/PRRSV coͲinfection rate was 7.6%.
In our data, we had 67 abortion cases, and 40% of them were PCV3 positive. In one abortion case investigation, histological lesions were observed in lung tissue of aborted fetus and PCV3 in-situ hybridization showed presence of PCV3 in the lesion.
Seven PCV3 whole genome sequences were obtained. Current PCV3 genomes in the U.S shared over 98% nucleotide identities. U.S strains did not cluster together and were grouped with PCV3 sequences obtained in other countries.
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
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.
Seasonal patterns can be seen in different cohorts located in different regions.
A comparison from a prevalence standpoint between the cohort of farms belonging to the 13 systems participating at the start of the MSHMP (CS) and the cohort of farms from systems that joined the program later (CL), was performed with the objective of assessing whether the patterns between cohorts compare.
As seen in Figure 1–CS, there was a clear shift towards more use of MLV over LVI for sow herd stability purposes. The proportion of farms using LVI in the CS versus the CL is 5% and 10%, respectively. When assessing the proportion of farms in each AASV PRRS category (Holtkamp et al., 2011) both groups are comparable (Table 1). Also the temporal pattern of infection can be seen in both cohorts as described by Tousignant et al (2014).
In summary, both cohorts of farms (CS versus CL) yield similar results which continue to highlight the robustness of the program and the representativeness of the systems contributing to this program.