The Potential of Real-time RT-PCR Cycle Threshold Values as a Predictor of COVID-19 Spread (Preprint)

Abstract

Coronavirus disease 2019 (COVID-19) testing is essential, as it enables early detection and halting the spread of infection throughout the community. Real-time reverse-transcriptase polymerase chain reaction (real-time RT-PCR) testing is the predominant method for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in samples, and the cycle threshold value (Ct value) is used to determine COVID-19 positivity.

The aim of the present study was to examine time series distribution during the COVID-19 pandemic, using Ct values at the national level in Korea, and analyze the association between Ct values and the time-varying reproduction number (Rt) to discuss the utilization of laboratory-based COVID-19 test results.

We used real-time RT-PCR results collected by the Seegene Medical Foundation from the index case in Korea between February 2020 and January 2022. The distribution of daily Ct values (RdRp/S target) was examined and compared with the daily count of newly diagnosed cases and Rt to determine the usability of Ct values. Moreover, time lag was applied to the daily count of newly diagnosed cases to analyze the association between Ct values and Rt.

During the COVID-19 pandemic, Ct values declined in general, while viral load increased progressively. After Ct values dropped markedly, the number of newly diagnosed cases rose substantially, and the association analysis confirmed that the daily count of newly diagnosed cases declined with increasing Ct values. The time series trend of the Ct values was also similar to that of Rt, a classic marker that was used as a predictor of pandemic trends; when compared to the actual count of newly diagnosed cases, we found that Ct values could be used to predict new diagnoses earlier than Rt. The fact that the Ct values were more sensitive to a substantial rise of new COVID-19 cases than Rt in the early days of the pandemic also supports this finding.

We examined the potential of Ct values as a predictor of new COVID-19 cases in real-time using nationally collected Ct value data. Further, we proposed the use of Ct values as an index reflecting the degree of viral load. The findings of this study can be used to establish indicators with higher predictive power than conventional outbreak predictions.