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.