Monitor reproduction parameters to assess PRRSV outbreaks

Researchers demonstrate that monitoring production data with statistical process control is feasible on breeding herds to identify significant variations.

April 4, 2017

3 Min Read
Monitor reproduction parameters to assess PRRSV outbreaks
National Pork Board

By Gustavo Silva and Daniel Linhares, Iowa State University Veterinary Diagnostic and Production Animal Medicine-College of Veterinary Medicine; and Robert Morrison, University of Minnesota Department of Veterinary Population Medicine-College of Veterinary Medicine

Porcine reproductive and respiratory syndrome virus remains a challenge causing substantial economic losses due to significant changes in productivity. The economic impact of PRRSV is due, in part, to increases in abortions, preweaning mortality and neonatal losses (mummified and/or stillbirth fetuses). Efficient ongoing surveillance programs enable early detection of pathogen introduction in breeding herds allowing rapid response by adopting practices to control pathogen spread within infected herds and to prevent transmission to other sites and/or production systems at risk that share equipment/supplies (e.g. trucks, trailers, food, etc.).

Conventional tools applied to monitoring herd PRRSV status are based on routine diagnostic testing to detect PRRSV by polymerase chain reaction (nucleic acid) and/or by serology (anti-PRRSV-antibodies) using blood serum samples or oral fluids. Since PRRSV infection in breeding herds is often characterized by significant changes in productivity, production parameters can be used as indicators of animal health condition and performance. Once a deviation in production parameters is detected, diagnostic tests are essential to confirm/rule out the role of specific pathogens.

Statistical process control (SPC) methods can be used to detect significant variation on productivity using a standard and consistent system. SPC signals can be used as a trigger and provide helpful orientation to production managers and veterinarians by identifying significant changes in productivity, which may be related to pathogen introduction enabling the quantification of the production and economic impact of outbreaks.

To evaluate the benefits of SPC methods in the swine industry, we gathered weekly production and PRRS-status information for 108 weeks of 14 breeding herds and applied a statistical process control method to assess relationship of significant changes in production records to document changes in PRRS status over time (Table 1). We monitored changes on weekly number of abortions, pre-weaning mortality rate (PWM) and prenatal losses (difference between “total number of pigs born per litter” and “number of pigs born alive per litter”).


The production system reported 10 PRRS outbreaks during the observation period. Early detection rate was higher for abortions and PWM (both at 90%) compared to that of prenatal losses (71%) (Table 1). Abortion had the earliest time to detect (TTD), detecting outbreaks up to four weeks prior to the reported date in the Swine Health Monitoring Project database. Prenatal losses were a poor indicator for early PRRS detection. Monitoring abortions and PWM yielded a higher sensitivity and specificity to signal a PRRS outbreak than prenatal losses (Table 1).


Results showed that the disease monitoring system in place at the studied production system (14 breeding herds) was effective to detect and report economically significant outbreaks. Our results also showed that systematically monitoring key production indicators led to detection of PRRS outbreaks with good accuracy (Table 1). Abortions were the most efficient parameter, detecting outbreaks up to four weeks before it was recorded in the SHMP database.

Furthermore, we demonstrated that monitoring production data with SPC is feasible on breeding herds to identify significant variations. Early detection of variation on production records allows producers to rapidly respond to infectious and non-infectious causes of variation in productivity.

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