Understanding and interpreting PRRSV diagnostics in the context of “disease transition stages”

Author links open overlay panelAlexandra Henao-Diaz  JuJi  Luis Giménez-Lirola  David H. Baum. Jeffrey Zimmerman

Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA 50011. USA

https://doi.org/10.1016/j.rvsc.2020.04.023

Highlights

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PRRSV infection is characterized by changes in its tissue distribution over time.

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Therefore, the rate of PRRSV detection varies over time by specimen-assay selection.

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The choice of specimen-assay must be tailored to specific PRRSV testing objectives.

Abstract

Herein we review broad issues that affect test performance for agents that produce persistent infections. Using PRRSV as an example, the relationship between “disease transition stages” and “diagnostic transition stages” is discussed using meta-analyses of diagnostic data (n = 4307 results) from the refereed literature to highlight the key issues. Although diagnostic technology will continue to improve, it may be concluded from the analysis that there can be no single best diagnostic approach; rather, the choice of specimen and test must be tailored to the specific testing objective. In most cases, meeting the testing objective(s) will require the use of more than one assay and/or specimen type.

Prediction of seasonal patterns of PRRSV detection in the USA

Prediction of seasonal patterns of porcine reproductive and respiratory syndrome virus RNA detection in the U.S. swine industry

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Giovani Trevisan, Leticia C. M. Linhares, Bret Crim, 

Poonam Dubey, Kent J. Schwartz, Eric R. Burrough, Chong Wang, Rodger G. Main, Paul Sundberg, Mary Thurn, Paulo T. F. Lages, Cesar A. Corzo, Jerry Torrison, Jamie Henningson, Eric Herrman, Gregg A. Hanzlicek, Ram Raghavan, Douglas Marthaler, Jon Greseth, Travis Clement, Jane Christopher-Hennings, David Muscatello, Daniel C. L. Linhares¹

First Published April 10, 2020 Research Article 

https://doi.org/10.1177/1040638720912406

Article information

bstract

We developed a model to predict the cyclic pattern of porcine reproductive and respiratory syndrome virus (PRRSV) RNA detection by reverse-transcription real-time PCR (RT-rtPCR) from 4 major swine-centric veterinary diagnostic laboratories (VDLs) in the United States and to use historical data to forecast the upcoming year’s weekly percentage of positive submissions and issue outbreak signals when the pattern of detection was not as expected. Standardized submission data and test results were used. Historical data (2015–2017) composed of the weekly percentage of PCR-positive submissions were used to fit a cyclic robust regression model. The findings were used to forecast the expected weekly percentage of PCR-positive submissions, with a 95% confidence interval (CI), for 2018. During 2018, the proportion of PRRSV-positive submissions crossed 95% CI boundaries at week 2, 14–25, and 48. The relatively higher detection on week 2 and 48 were mostly from submissions containing samples from wean-to-market pigs, and for week 14–25 originated mostly from samples from adult/sow farms. There was a recurring yearly pattern of detection, wherein an increased proportion of PRRSV RNA detection in submissions originating from wean-to-finish farms was followed by increased detection in samples from adult/sow farms. Results from the model described herein confirm the seasonal cyclic pattern of PRRSV detection using test results consolidated from 4 VDLs. Wave crests occurred consistently during winter, and wave troughs occurred consistently during the summer months. Our model was able to correctly identify statistically significant outbreak signals in PRRSV RNA detection at 3 instances during 2018.

Keywords cyclic, outbreak signal, PRRSV, prediction, swine pathogens, veterinary diagnostic laboratories