Systematic Review on the Correlation Between SARS-CoV-2 Real-Time PCR Cycle Threshold Values and Epidemiological Trends

Abstract

Background The ability to proactively predict the epidemiological dynamics of infectious diseases such as coronavirus disease 2019 (COVID-19) would facilitate efficient public health responses and may help guide patient management. Viral loads of infected people correlate with infectiousness and, therefore, could be used to predict future case rates. Aim In this systematic review, we determine whether there is a correlation between severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) real-time reverse-transcription polymerase chain reaction (RT–PCR) cycle threshold (Ct) values (a proxy for viral load) and epidemiological trends in patients diagnosed with COVID-19, and whether Ct values are predictive of future cases. Methods A PubMed search was conducted on August 22 2022, based on a search strategy of studies reporting correlations between SARS-CoV-2 Ct values and epidemiological trends. Results Data from 16 studies were relevant for inclusion. RT–PCR Ct values were measured from national ( n = 3), local ( n = 7), single-unit ( n = 5), or closed single-unit ( n = 1) samples. All studies retrospectively examined the correlation between Ct values and epidemiological trends, and seven evaluated their prediction model prospectively. Five studies used the temporal reproduction number ( R t ) as the measure of the population/epidemic growth rate. Eight studies reported a prediction time in the negative cross-correlation between Ct values and new daily cases, with seven reporting a prediction time of ~1–3 weeks, and one reporting 33 days. Conclusion Ct values are negatively correlated with epidemiological trends and may be useful in predicting subsequent peaks in variant waves of COVID-19 and other circulating pathogens. Supplementary Information The online version contains supplementary material available at 10.1007/s40121-023-00772-7.

Snapshot of Anti-SARS-CoV-2 IgG Antibodies in COVID-19 Recovered Patients in Guinea

“…No significant correlation was observed between the level of IgG antibodies and the viral load at the original diagnosis (CT value) nor with the duration of positivity. If the Ctvalue thresholds are important biological data in COVID-19 management, their relevance as predictors for disease outcome remains debated [36][37][38]. It was previously demonstrated that patients may show a rapid decline in antibody production quickly after the onset of infection [39] and this is possibly the case for the patients we tested as negative (36%: 54/200).…”

Effective real-time transmission estimations incorporating population viral load distributions amid SARS-CoV-2 variants and pre-existing immunity

“…6 Therefore, our approach employing the initial Ct post-confirmation may be minimally affected by variants and vaccinations, consistent with previous studies measuring the transmission using various measurements of transmission (e.g., growth rate). 5,26–28…”

“…3 We have since applied a simplified method to incorporate the temporal population Ct distribution into real-time transmission estimation, measured by the effective reproductive number R. 4 While these studies have significantly advanced our understanding of the association between population viral shedding and transmission, they were performed during early waves of the COVID-19 pandemic, and did not account for the viral evolution and immunity derived from natural infections and/or vaccinations. 5 The generalizability of the identified association to epidemics remained under-investigated, especially in the context of the emerging SARS-CoV-2 variants (e.g., Omicron) and increasing pre-existing immunity, which were found to be associated with shorter duration in viral shedding clearance at individual level. 6 Additionally, large epidemics with exponential increase in cases could soon exceed testing and surveillance capacity and may further prolong the delays between infection and being diagnosed due to the constrained resources, making it more challenging to derive timely and reliable R t estimates using conventional incidence-based approaches.…”

Physical Simulation-Based Calibration for Quantitative Real-Time PCR

“…The Ct value is negatively correlated with the initial viral load, allowing for indirect quantification of the starting virus copy number [19,20]. Typically, due to variations in reported Ct values from different PCR methods, it is necessary to use a standard curve for accurate quantification of expected virus copy number [21]. The standard curve produced by qPCR is a well-established method for the quantification of nucleic acids [22], in the absence of a gold standard test, the standard curve method is often used to construct and verify the reliability of qPCR instruments for nucleic acid detection [23,24].…”

“…The standard curve produced by qPCR is a well-established method for the quantification of nucleic acids [22], in the absence of a gold standard test, the standard curve method is often used to construct and verify the reliability of qPCR instruments for nucleic acid detection [23,24]. Moreover, following the COVID-19 pandemic, the importance of Ct values has been further emphasized, with many studies focusing on exploring the correlation between Ct values and disease severity and transmissibility [25,26], as well as the correlation between Ct values and epidemiological trends [21]. In addition to the content of the tested samples, Ct values are also influenced by factors such as the temperature and optical performance of the qPCR instrument itself [27], the use of reagent kits, sample collection time, primer specificity, amplification efficiency, sample inactivation temperature, and inactivation time [28].…”