2.3 ELISA cut-off estimation
A subset of the sample comprising positive (n=51) and negative (n=114) sera from symptomatic and asymptomatic hares that tested positive and negative for MYXV-DNA by qPCR-M000.5R/L, was used to assess the ELISA cut-off using the R (R Development Core Team, 2008) package ‘OptimalCutpoints’ (López-Ratón et al., 2014). The cut-off was estimated based on the criteria of a positive predictive value (PPV) >0.95, to guarantee that the animals testing positive were truly exposed to MYXV or to an antigenically related virus.
To further support the estimated cut-off, we performed a finite mixture analysis of log-transformed RI10 data, to estimate the mean and standard deviation of the negative population (Peel et al., 2014). Finite mixture models allow to characterize the distributions of subgroups within bimodal datasets (Benaglia et al., 2009), thus being an alternative tool to estimate the cut-off of serological tests in the absence of reference tests (Peel et al., 2014). Finite mixture models were implemented using the expectation–maximization algorithm for mixtures of univariate normal distributions, from the package ‘mixtools’ (Benaglia et al., 2009) in R (R Development Core Team, 2017). The proportion of the seronegative subgroup was set at 1-prevalence and the mean of each subgroup was defined from the mean logRI10 of the subgroup in which molecular testing was performed.