After being introduced in 1999, PRRS was eradicated from the country in 2012.
In 2013 PRRS was again detected, sequence analysis suggested this was a new introduction to the country.
The Chilean swine industry and the Chilean Veterinary Services (SAG) expect to again eliminate the disease in the near future.
PRRS is a notifiable disease in Chile. It was first detected in 1999, and in 2000 both the swine industry and government joined efforts to eradicate the disease by a series of coordinated events including a mixture of herd closure and depopulation of infected premises. Vaccination was not allowed in the country to control PRRSV infection. The eradication program was completed in 2007 and as a result, Chile was declared PRRSV free in 2012. Nevertheless, on October 2013 clinical signs compatible with PRRSV were reported in a commercial sow farm. Since then, all commercial herds performed surveillance activities according to a risk score based on location and biosecurity measures. From October 2013 to October 2017, approximately 153,000 blood samples have been analyzed.
Viral sequences obtained during the 2013 outbreak were compared to sequences from the early 2000s outbreak in Chile. Results showed a large genetic difference between isolates from both outbreaks. Further analyses demonstrated that the Chilean virus was closely related to a virus circulating in the state of Indiana in the US at the time of introduction. These results suggested that the latest PRRSV outbreak in Chile was most likely due to a new introduction into the country rather than a reemergence of a strain previously detected in Chile.
By October 2017, the disease was restricted to approximately 45,000 animals in six commercial farms owned by two companies that currently have eradication programs in place. These six infected commercial sites are clustered in three areas. (See figure above)
Today, we are sharing a publication from the Preventive Veterinary Medicine journal, by Dr. Andres Perez and the STEMMA laboratory. The goal of the study was to quantify the combined effect of factors such as season and herd size on the spatial range of high-risk areas for PRRSV outbreaks. Using Species Distribution Model, the team extracted associations between hypothesized risk factors and disease occurrence.
A species distribution model was used, to predict the spatial risk of PRRSv in swine populations across the U.S.
All of the Maxent spatial models identified high-risk areas, with probabilities greater than 0.5.
Relative contribution of pig density to PRRSv risk was higher in densely pig populated areas.
Relative contribution of climate and land cover to PRRSv risk were important in areas with relative low pig densities.
Ecological dynamics of PRRSv are different between swine production region in the U.S.
The largest number of PRRSv outbreaks in the U.S., as reported in the MSHMP, was observed in north central parts of Iowa, followed by south central areas of Minnesota. However, our crude U.S. Maxent model identified eastern North Carolina, southern Minnesota, and northern Iowa as high-risk areas for PRRSv outbreaks. As expected, pig density accounted for most of the PRRSv spatial risk (81.3% relative contribution). Climate (interpreted as the percentage of day-to-night temperature oscillation compared with the summer-to-winter oscillation, and mean temperature of the warmest quarter) accounted for the remaining spatial risk. Overall, the crude Maxent model suggested geographical areas with high pig densities and with a low level of daily temperature variability to the year are mostly suitable for circulation and maintenance of PRRSv.
The model for the South East region indicated that pig density was the most important predictor; followed by precipitation of the wettest month, land cover, and temperature seasonality. The relative contribution of pig density was smaller for this region compared to the Midwest. Specifically, geographical locations with high pig density, precipitation amount between 120 and 200 mm during the wettest months, and that were located within croplands were mostly suitable for PRRSv outbreaks in North Carolina and Northern South Carolina.
Additionally, the spread of PRRSV under certain conditions was more evident for the regions where pig density is relatively low. For example, in Illinois and Indiana and Kansas, Colorado, Oklahoma and Texas, wet weather and temperatures above 0 °C were more important in predicting the spatial risk of PRRSv than pig density.
Porcine reproductive and respiratory syndrome virus (PRRSv) outbreaks cause significant financial losses to the U.S. swine industry, where the pathogen is endemic. Seasonal increases in the number of outbreaks are typically observed using PRRSv epidemic curves. However, the nature and extent to which demographic and environmental factors influence the risk for PRRSv outbreaks in the country remains unclear. The objective of this study was to develop risk maps for PRRSv outbreaks across the United States (U.S.) and compare ecological dynamics of the disease in five of the most important swine production regions of the country. This study integrates spatial information regarding PRRSv surveillance with relevant demographic and environmental factors collected between 2009 and 2016. We used presence-only Maximum Entropy (Maxent), a species distribution modeling approach, to model the spatial risk of PRRSv in swine populations. Data fitted the selected model relatively well when the modeling approach was conducted by region (training and testing AUCs < 0.75). All of the Maxent models selected identified high-risk areas, with probabilities greater than 0.5. The relative contribution of pig density to PRRSv risk was highest in pig-densely populated areas (Minnesota, Iowa and North Carolina), whereas climate and land cover were important in areas with relatively low pig densities (Illinois, Indiana, South Dakota, Nebraska, Kansas, Oklahoma, Colorado, and Texas). Although many previous studies associated the risk of PRRSv with high pig density and climatic factors, the study here quantifies, for the first time in the peer-reviewed literature, the spatial variation and relative contribution of these factors across different swine production regions in the U.S. The results will help in the design and implement of early detection, prevention, and control strategies for one of the most devastating diseases affecting the swine industry in the U.S.
Systematic monitoring of key production performance indicators allowed for early detection of PRRS outbreaks.
Number of abortions was the most efficient parameter, detecting outbreaks up to 4 weeks before being reported to MSHMP.
Early detection of signals associated with disease outbreaks may help in preventing further spread of the virus to other herds, and allowing implementation of rapid response intervention(s).
Two-years worth of reproductive performance data from a production system with 14 breeding herds (1,512 herd weeks) was gathered. Weekly data on number of abortions, pre-weaning mortality (PWM) and difference between total born and born alive (neonatal losses), were merged with weekly MSHMP PRRSV status. A statistical process control method was used to scan production data for significant deviations from baseline.
The time-to-detect outbreak, percentage of early detection of PRRSv-associated productivity deviations, and relative sensitivity and specificity of the production data monitoring system were determined relative to the MSHMP.
Abortion signals were detected 1 to 4 weeks before outbreaks were reported to the MSHMP. Most pre-weaning mortality signals coincided with the outbreak date reported to the MSHMP, and prenatal losses signals were detected from 1 to 3 weeks after the MSHMP reported outbreak date. Overall, the models had high relative sensitivity (range 85.7 to 100%) and specificity (range 98.5% to 99.6%) when comparing to the changes in
PRRS status reported in the MSHMP database.
We launched a new series on the blog last month. Once a month, we are sharing with you a presentation given at the 2017 Allen D. Leman swine conference, on topics that the swine group found interesting, innovative or that lead to great discussions.
Our second presentation today is from Dr. Paul Yeske from Swine Vet Center, who is coming back on his experience with Mycoplasma hyopneumoniae elimination and giving us an update if the herds stayed negative.
To listen to this presentation, please click on the picture below:
Our latest collaboration with the National Hog Farmer was written by Drs. Montse Torremorell and Marie Culhane from the University of Minnesota.
Flu never seems to go away in some herds and that is because there are groups of pigs, or subpopulations, that are able to maintain and spread the flu virus.
One of the most important subpopulations that have been identified as sources of virus on a farm is the piglets. Piglets may be infected, but may not show any signs of disease, and as a result, are silent spreaders of flu. Then, at weaning, a small, but significant, percentage of the piglets can be subclinically infected with flu, meaning they appear healthy but are shedding flu at the nursery or wean-to-finish site.
This causes a challenge for producers because even though piglets are born free of flu, they tend to be contaminated by the dam during their second week of like. The peak of flu-positive piglets occurs at weaning when piglets are moved to a nursery where they may be put in contact with naive piglets from another source and therefore become a major source of infection.
We need to understand how piglets become infected in the farrowing room in order to prevent it. Sow vaccination is a tool commonly used to protect piglets via the transmission of antibodies through the colostrum or maternal immunity. It has been shown to decrease the prevalence of flu-positive piglets at weaning but is insufficient to constantly wean negative animals.
“At the University of Minnesota, we have been measuring the impact of piglets on the spread of flu for years. We found, in a study by Allerson of 52 swine breeding herds in the United States, 23 herds (44%) tested IAV RT-PCR positive at least once during a six-month study period. Groups of piglets from those herds also tested positive for flu at weaning about 25% (75 of 305) of the time.
Along those same lines, Chamba and partnering sow farms reported that out of the 34 farms studied for more than five years, all sow farms tested positive for flu at one time or another and the level of flu infection in the groups of weaned pigs ranged from 7% to 57%. More importantly, in this study, approximately 28% (427 of 1,523) of groups of pigs tested positive at weaning. […]
Ultimately, the successful control of on-going flu infections in growing pigs will depend on the sow farm’s ability to wean a negative pig […]”
Naturally-infected boars have been documented to shed Senecavirus A (SVA) RNA in semen for up to three months after exhibiting vesicular disease.
Experimentally-infected boars shed SVA RNA in semen for up to three weeks post-inoculation.
The majority of experimentally-infected boars did not exhibit clinical signs or develop apparent lesions.
“This update shows that SVA RNA is shed in semen from both naturally-infected and experimentally-inoculated boars. The prolonged shedding of viral RNA in semen and the presence of SVA RNA in the testes and tonsils of the naturally-infected boars for up to three months are concerning findings and raises the possibility of persistent infection in boars. While the duration of shedding in semen for the experimentally-infected boars was considerably shorter than for the naturally-infected boars, the fact that all contemporary-strain boars had PCR-positive semen on at least one collection indicate that shedding in semen is a repeatable phenomenon and shedding occurred in some boars which did not exhibit clinical signs or develop vesicular lesions. It is currently unknown whether semen from infected boars can serve as a source of infection if used to inseminate susceptible females.”
The Torremorell lab is continuing to explore swine influenza epidemiology in this recent publication from Dr. Fabian Chamba Pardo in Frontiers in Veterinary Science. After showing that multiple genome constellations of similar and distinct influenza viruses co-circulate in pigs, the group is now presenting new data about influenza herd-level prevalence in the Midwest, and how it is influenced by seasons. Click on the banner below to read the entire research article.
60 sow farms from a single Midwestern production system were enrolled in this study. Between one and seven oral fluid samples were collected at each farm weekly and meteorological data (air temperature and relative humidity) was compiled from stations located from the farms.
28% of submissions had at least one influenza positive result. All farms tested positive at least once during study period. Herd-level prevalence ranged from 7% to 57% as show in the figure above. Prevalence was low in summer, rose during fall, and peaked twice in both early winter (December) and late spring (May). August was the month with the lowest prevalence. Influenza herd-level prevalence was higher when both mean outdoor air temperature and air humidity were lower.
The most common clades identified were H1 delta 1, H1 gamma 1, and clusters H3 IV A and H3 IV B. Furthermore, 21% of the farms had 3 different influenza genetic clades circulating during the study period and 18% had 2.
Influenza is a costly disease for pig producers and understanding its epidemiology is critical to control it. In this study, we aimed to estimate the herd-level prevalence and seasonality of influenza in breed-to-wean pig farms, evaluate the correlation between influenza herd-level prevalence and meteorological conditions, and characterize influenza genetic diversity over time. A cohort of 34 breed-to-wean farms with monthly influenza status obtained over a 5-year period in piglets prior to wean was selected. A farm was considered positive in a given month if at least one oral fluid tested influenza positive by reverse transcriptase polymerase chain reaction. Influenza seasonality was assessed combining autoregressive integrated moving average (ARIMA) models with trigonometric functions as covariates. Meteorological conditions were gathered from local land-based weather stations, monthly aggregated and correlated with influenza herd-level prevalence. Influenza herd-level prevalence had a median of 28% with a range from 7 to 57% and followed a cyclical pattern with levels increasing during fall, peaking in both early winter (December) and late spring (May), and decreasing in summer. Influenza herd-level prevalence was correlated with mean outdoor air absolute humidity (AH) and temperature. Influenza genetic diversity was substantial over time with influenza isolates belonging to 10 distinct clades from which H1 delta 1 and H1 gamma 1 were the most common. Twenty-one percent of farms had three different clades co-circulating over time, 18% of farms had two clades, and 41% of farms had one clade. In summary, our study showed that influenza had a cyclical pattern explained in part by air AH and temperature changes over time, and highlighted the importance of active surveillance to identify high-risk periods when strategic control measures for influenza could be implemented.