Monitoring SARS-CoV-2 variant transitions using differences in diagnostic cycle threshold values of target genes

Monitoring the emergence of new SARS-CoV-2 variants is important to detect potential risks of increased transmission or disease severity. We investigated the identification of SARS-CoV-2 variants from real-time reverse transcriptase polymerase chain reaction (RT-PCR) routine diagnostics data. Cycle threshold (Ct) values of positive samples were collected from April 2021 to January 2022 in the Northern Metropolitan Area of Barcelona (n = 15,254). Viral lineage identification from whole genome sequencing (WGS) was available for 4618 (30.3%) of these samples. Pairwise differences in the Ct values between gene targets (delta Ct) were analyzed for variants of concern or interest circulating in our area. A specific delay in the Ct of the N-gene compared to the RdRp-gene (delta Ct(NR)) was observed for Alpha, Delta, Eta and Omicron. Temporal differences in delta Ct(NR) correlated with the dynamics of viral replacement of Alpha by Delta and of Delta by Omicron according to WGS results. Using delta Ct(NR), prediction of new variants of concern at early stages of circulation was achieved with high sensitivity and specificity (91.1% and 97.8% for Delta; 98.5% and 90.8% for Omicron). Thus, tracking population wide trends in delta Ct values obtained from routine diagnostics testing in combination with WGS could be useful for real-time management and response to local epidemics.

Automated summary

Monitoring the emergence of new SARS-CoV-2 variants is crucial for identifying potential risks. This study investigated the use of real-time reverse transcriptase polymerase chain reaction (RT-PCR) routine diagnostics data to identify variants of concern. By analyzing the cycle threshold (Ct) values and conducting whole genome sequencing (WGS), the study achieved high sensitivity and specificity in predicting the presence of new variants at early stages of circulation. Tracking the trends of Ct values in the population, combined with WGS, could be valuable for real-time management and response to local epidemics.