Bayesian latent class model estimates of diagnostic accuracy for three test methods designed to detect spring viremia of carp virus.

Spring viremia of carp virus (SVCV) causes a systemic hemorrhagic disease that poses a significant risk to wild and cultured fish and is listed as notifiable by the World Organization for Animal Health. Validated molecular diagnostic tools for SVCV are required to accurately describe and analyze the ecology of the virus. Here, the diagnostic specificity (DSp) and sensitivity (DSe) (i.e. accuracy) of three SVCV diagnostic tests - 2 reverse transcription quantitative polymerase chain reaction (RT-qPCR) assays Q1G and Q2N and virus isolation by cell culture (VI) - were evaluated using 2-class latent class models run in maximum likelihood (ML) and Bayesian frameworks. Virus-free or experimentally-infected koi were sorted into three populations with low, moderate or high prevalence levels of SVCV (n = 269 fish in total). Koi kidney tissues were tested using Q2N and Q1G and for the VI assay, pools of kidney, spleen and gill tissues were used. All samples were blinded and analyzed in one laboratory. The ML and Bayesian approaches successfully estimated the diagnostic accuracy of the 3 tests with the exception of 1 ML model. The estimates were consistent across the two frameworks. The DSe estimates were higher for Q1G (>98 %) and Q2N (>96 %) compared to VI (>60 %). The DSp of all three tests varied by 12-15 % (79-91 % for Q1G, 79-94 % for Q2N and 81-97 % for VI) across same-fish samples revealing the potential range in test performance for one sample. The 3 fish populations had distinct SVCV prevalence levels estimated at 0-3 % (low), 70-73 % (moderate) and 95-96 % (high). The Bayesian covariance models revealed minor DSe dependence between Q1G and Q2N. The results suggested that SVCV diagnostic tests Q2N and Q1G are suitable for use as diagnostic assays and are fit for presumptive diagnosis, surveillance, and certification of populations or individuals as SVCV free.