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.
Piglet birth weight and colostrum intake are positively associated with pre-weaning survival and weaning weight.
Compared to piglets of similar birth weight, piglets with greater weight gain within the first day of life showed improved average daily feed intake and average daily gain in finishing and required fewer days on feed to reach market weight.
The study followed 808 piglets from birth to weaning and measured birth weight, colostrum intake, weight at 24 hours of life and weight at weaning. Results showed that a 1 lb increase in birth weight resulted in a 2.8 lb increase in weaning weight and increased piglet survival chances. Similarly, a 1 g increase in colostrum intake was associated with an 8.8 g increase in weaning weight.
To study consequences of early-life parameters on later stages of production, feed intake was recorded for 448 piglets from 74 days of age until the average pen weight reached 265 lb. Results showed that in both the low and high birth weight, a high colostrum intake increased the average daily gain and decreased the age at market.
From Friday to Sunday, the North American PRRS Symposium will be happening in the InterContinental hotel in Chicago. This annual meeting, held on the first week-end of December in conjunction with the National Swine Improvement Federation, is for scientists, diagnosticians, practitioners and producers who are interested in porcine reproductive and respiratory syndrome virus (PRRSV), the most costly viral disease to ever face a global swine industry. The meeting is further expanded to include emerging and foreign animal diseases, such as Seneca Valley virus (SVV), porcine epidemic diarrhea virus (PEDV), porcine circovirus-associated disease (PCVAD), African swine fever virus (ASFV), classical swine fever virus (CSFV), and other high-consequence diseases of swine. Scientific topics include disease control, vaccines, pathogenesis, diagnostics, epidemiology and host genetics. This year meeting is dedicated to our friend and colleague Dr. Bob Morrison. The program of the conference is available online.
From the University of Minnesota, Dr. Montse Torremorell will be moderating a session regarding PRRS on the field whereas Dr. Cesar Corzo, Leman chair in swine health and productivity and Dr. Perle Boyer will be presenting respectively on the Morrison Swine Health Monitoring Program and PRRS genetic resistance: an online class for swine experts.
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.