1 INTRODUCTION
In South Korea, the first case of COVID-19 was confirmed on January 20, 2020, and the government established its response measures based on aggressive testing, tracing, and quarantine and isolation. Of these strategies, aggressive testing is at the core for preventing the spread of infection through the community by enabling early detection of infection, and to facilitate testing, the government launched many screening testing sites, implemented mobile sample collection teams and mobile health examination services, continually increased diagnostic testing counts, and added COVID-19 to the existing respiratory disease surveillance system. As a result, mass COVID-19 tests were done with quick turnaround times, and the government performed tracing, enforced quarantine, and provided treatment based on the test results; for this, South Korea was considered to have done well in COVID-19 control compared to other countries (Lee, 2020).
In South Korea, COVID-19 tests are primarily performed via the 2019 Novel coronavirus Real-time reverse-transcriptase polymerase chain reaction (Real-time RT-PCR) to detect viral genes, and upper airway and lower airway samples are generally used. The assay is designed to detect two or three regions of SARS-CoV-2 genes that encode nucleocapsid (N ), envelope (E ), or spike (S ) proteins or RNA-dependent RNA polymerase (RdRp ) gene. Of various results generated by Real-time RT-PCR, cycle threshold (Ct) values, which is the number of cycles of amplification required to cross a certain threshold, are used to determine COVID-19 positivity. A sample is deemed positive when the Ct value of the target gene is equal to or smaller than the cutoff. Because this value can also be understood as the viral load, a lower Ct value is interpreted as a higher viral load.
Many recent studies have utilized Ct values in their investigation, primarily the association between Ct values and demographic factors (Ade et al., 2021; Chung et al., 2021), disease severity (Bustos et al., 2021), and mortality rate (Miller et al., 2021; Waudby-West et al., 2021). However, these studies are limited in the fact that they used positive COVID-19 samples from small regions or Ct values from hospitalized patients.
On the other hand, studies have also utilized Ct values in predicting future infection transmission trends and monitoring infectious diseases. During an epidemic, investigating the transmission trend and predicting the size of outbreaks present valuable data for devising response measures against the epidemic. Currently, time-varying reproduction number (Rt) is used as an index for predicting the trends of an epidemic. However, Rt is dependent on the number of confirmed cases, and one key shortcoming of the index is that it cannot provide real-time prediction of the size of an epidemic due to the delay between the latent period of a disease and reporting of confirmed cases. To overcome such limitation of Rt, a few studies showed that large population distributions of Ct values can be used to predict epidemic trends (Lin et al., 2022; Yin et al., 2021). Furthermore, some studies added distributions of Ct values to the existing Rt calculation to generate more accurate predictions and compared their predictive accuracies (Alizon et al., 2022; Hay et al., 2021). However, these studies utilized Ct values collected from a short period or simply modeled their results using Ct values obtained through a simulation, and the said studies also analyzed the correlation between Ct values and prevalence, such as number of severe cases of infection, as opposed to the incidence rate.
Therefore, this study aims to investigate the long-term time series distribution of Ct values from nationally representative data and analyze the associations with the number of new cases and Rt trends to discuss the use of laboratory-based COVID-19 test results.