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The identification and isolation of highly infectious SARS-CoV-2-infected individuals is an important public health strategy. Rapid antigen detection tests (RADT) are promising tools for large-scale screenings due to timely results and feasibility for on-site testing. Nonetheless, the diagnostic performance of RADT in detecting infectious individuals is not yet fully determined. In this study, RT-qPCR and virus culture of RT-qPCR-positive samples were used to evaluate and compare the performance of the Standard Q COVID-19 Ag test in detecting SARS-CoV-2-infected and possibly infectious individuals. To this end, two combined oro- and nasopharyngeal swabs were collected at a routine SARS-CoV-2 diagnostic center. A total of 2,028 samples were tested, and 118 virus cultures were inoculated. SARS-CoV-2 infection was detected in 210 samples by RT-qPCR, representing a positive rate of 1036%. The Standard Q COVID-19 Ag test yielded a positive result in 92 (4.54%) samples resulting in an overall sensitivity and specificity of 42.86 and 99.89%, respectively. For adjusted C-T values of <20 (n = 14), <25 (n = 57), and <30 (n = 88), the RADT reached sensitivities of 100, 9825, and 88.64%, respectively. All 29 culture-positive samples were detected by the RADT. Although the overall sensitivity was low, the Standard Q COVID-19 Ag test reliably detected patients with high RNA loads. In addition, negative RADT results fully corresponded with the lack of viral cultivability in Vero E6 cells. These results indicate that RADT can be a valuable tool for the detection of individuals with high RNA loads that are likely to transmit SARS-CoV-2.

We give a novel algorithm for finding a parsimonious context tree (PCT) that best fits a given data set. PCTs extend traditional context trees by allowing context-specific grouping of the states of a context variable, also enabling skipping the variable. However, they gain statistical efficiency at the cost of computational efficiency, as the search space of PCTs is of tremendous size. We propose pruning rules based on efficiently computable score upper bounds with the aim of reducing this search space significantly. While our concrete bounds exploit properties of the BIC score, the ideas apply also to other scoring functions. Empirical results show that our algorithm is typically an order-of-magnitude faster than a recently proposed memory-intensive algorithm, or alternatively, about equally fast but using dramatically less memory.

Inhomogeneous parsimonious Markov models have recently been introduced for modeling symbolic sequences, with a main application being DNA sequence analysis. Structure and parameter learning of these models has been proposed using a Bayesian approach, which entails the practically challenging choice of the prior distribution. Cross validation is a possible way of tuning the prior hyperparameters towards a specific task such as prediction or classification, but it is overly time-consuming. On this account, robust learning methods, which do not require explicit prior specification and – in the absence of prior knowledge – no hyperparameter tuning, are of interest. In this work, we empirically investigate the performance of robust alternatives for structure and parameter learning that extend the practical applicability of parsimonious Markov models to more complex settings than before.

Parsimonious Markov models, a generalization of variable order Markov models, have been recently introduced for modeling biological sequences. Up to now, they have been learned by Bayesian approaches. However, there is not always sufficient prior knowledge available and a fully uninformative prior is difficult to define. In order to avoid cumbersome cross validation procedures for obtaining the optimal prior choice, we here adapt scoring criteria for Bayesian networks that approximate the Normalized Maximum Likelihood (NML) to parsimonious Markov models. We empirically compare their performance with the Bayesian approach by classifying splice sites, an important problem from computational biology.