Association between RT-PCR Ct values and COVID-19 new daily cases: a multicenter cross-sectional study

Population-Level SARS-CoV-2 RT-PCR Cycle Threshold Values and Their Relationships with COVID-19 Transmission and Outcome Metrics: A Time Series Analysis Across Pandemic Years

This study investigates the relationship between SARS-CoV-2 RT-PCR cycle threshold (Ct) values and key COVID-19 transmission and outcome metrics across five years of the pandemic in Jalisco, Mexico. Utilizing a comprehensive time-series analysis, we evaluated weekly median Ct values as proxies for viral load and their temporal associations with positivity rates, reproduction numbers (Rt), hospitalizations, and mortality. Cross-correlation and lagged regression analyses revealed significant lead-lag relationships, with declining Ct values consistently preceding surges in positivity rates and hospitalizations, particularly during the early phases of the pandemic. Granger causality tests and vector autoregressive modeling confirmed the predictive utility of Ct values, highlighting their potential as early warning indicators. The study further observed a weakening association in later pandemic stages, likely influenced by the emergence of new variants, hybrid immunity, changes in human behavior, and diagnostic shifts. These findings underscore the value of Ct values as scalable tools for public health surveillance and highlight the importance of contextualizing their analysis within specific epidemiological and temporal frameworks. Integrating Ct monitoring into surveillance systems could enhance pandemic preparedness, improve outbreak forecasting, and strengthen epidemiological modeling.

SARS-CoV-2 variant survey: Comparison of RT-PCR screening with TGS and variant distribution across two divisions of Bangladesh

BACKGROUND

The widespread increase in multiple variants of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) since 2020 is causing significant health concerns worldwide. While whole-genome sequencing (WGS) has played a leading role in surveillance programs, many local laboratories lack the expertise and resources. Thus, we aimed to investigate the circulating SARS-CoV-2 variants and evaluate the performance of multiplexed real-time reverse transcription-PCR (RT-PCR) for screening and monitoring the emergence of new SARS-CoV-2 variants in Bangladesh.

METHODS

A total of 600 confirmed SARS-CoV-2-positive cases were enrolled either prospectively or retrospectively from two divisions of Bangladesh. The samples were screened by variant RT-PCR targeting five mutations of the spike gene (N501Y, P681R, L452R, E484K, E484Q). A subsample of the study population was also selected for third-generation sequencing (TGS) and the results were compared to the variant RT-PCR screening. An in-depth comparison was made between the two methods in terms of congruence and cost-benefit.

RESULT

Seven variants were detected among samples, with similar distributions of the variants across both divisions. Variant RT-PCR for the targeted mutations lead to a 98.5% call rate; only nine samples failed to be determined. No association was found regarding the demographic features, clinical criteria, or routine RT-PCR Ct values across the variants. The clade diversity of the sequenced subpopulation (n = 99) exhibited similar distributions across the two study sites and other epidemiologic variables. Variant RT-PCR successfully distinguished variants of concern (VOCs) and variants of interest (VOIs); however, 8% discrepancy was observed for the closest lineages. Moreover, the variant RT-PCR represented an ideal balance of cost, time, and accuracy that outweigh their limitations.

CONCLUSION

Based on the strong agreement of variant RT-PCR with TGS, such rapid, easily accessible approaches of rapid strain typing are essential in the context of pandemic responses to guide both treatment decisions and public health measures.

Kinetics of SARS-CoV-2 Viral Load in Hospitalized Patients

The rapid and accurate detection of infectious people is crucial in controlling outbreaks. The aim of this study was to evaluate the kinetics of the viral load expressed as Ct in COVID-19 hospitalized patients. Nasopharyngeal swab specimens were collected for RT-PCR testing. Forty-one subjects were recruited, of which 48.8% developed severe symptoms and 51.2% showed milder symptoms. The distribution of Ct values measured from the symptom onset showed that the kinetics of the viral load decreased with increasing time. A Ct of 25 (high viral load) was reached after a mean of 9.9 ± 4.8 days from the symptom onset, without a significant difference between patients with severe (10.9 ± 5.7 days) and milder (9.0 ± 3.9 days) symptoms. In 65.8% of cases, a high viral load was maintained for more than 7 days from the symptom onset, especially in patients with severe symptoms (70.6%). A Ct of 30 (moderate viral load) and of 38 (low viral load) were reached after a mean of 16.1 ± 8.1 and 28.5 ± 22.4 days from the symptom onset, respectively, with a significant difference between patients with severe (Ct = 30:17.9 ± 9.8 days; Ct = 38:34.6 ± 29.6 days) and milder (Ct = 30:14.3 ± 5.8 days; Ct = 38:22.7 ± 9.9 days) symptoms. These results provide an understanding of the viral kinetics of SARS-CoV-2 and have implications for pandemic control strategies and practices.

Liver injury in COVID-19: an insight into pathobiology and roles of risk factors

COVID-19 is a complex disease that can lead to fatal respiratory failure with extrapulmonary complications, either as a direct result of viral invasion in multiple organs or secondary to oxygen supply shortage. Liver is susceptible to many viral pathogens, and due to its versatile functions in the body, it is of great interest to determine how hepatocytes may interact with SARS-CoV-2 in COVID-19 patients. Liver injury is a major cause of death, and SARS-CoV-2 is suspected to contribute significantly to hepatopathy. Owing to the lack of knowledge in this field, further research is required to address these ambiguities. Therefore, we aimed to provide a comprehensive insight into host-virus interactions, underlying mechanisms, and associated risk factors by collecting results from epidemiological analyses and relevant laboratory experiments. Backed by an avalanche of recent studies, our findings support that liver injury is a sequela of severe COVID-19, and certain pre-existing liver conditions can also intensify the morbidity of SARS-CoV-2 infection in synergy. Notably, age, sex, lifestyle, dietary habits, coinfection, and particular drug regimens play a decisive role in the final outcome and prognosis as well. Taken together, our goal was to unravel these complexities concerning the development of novel diagnostic, prophylactic, and therapeutic approaches with a focus on prioritizing high-risk groups.

Cycle Threshold Values as Indication of Increasing SARS-CoV-2 New Variants, England, 2020-2022

Early detection of increased infections or new variants of SARS-CoV-2 is critical for public health response. To determine whether cycle threshold (Ct) data from PCR tests for SARS-CoV-2 could serve as an early indicator of epidemic growth, we analyzed daily mean Ct values in England, UK, by gene target and used iterative sequential regression to detect break points in mean Ct values (and positive test counts). To monitor the epidemic in England, we continued those analyses in real time. During September 2020-January 2022, a total of 7,611,153 positive SARS-CoV-2 PCR test results with Ct data were reported. Spike (S) gene target (S+/S-)-specific mean Ct values decreased 6-29 days before positive test counts increased, and S-gene Ct values provided early indication of increasing new variants (Delta and Omicron). Our approach was beneficial in the context of the first waves of the COVID-19 pandemic and can be used to support future infectious disease monitoring.

Epidemiology of COVID-19 and the Utility of Cycle Threshold (Ct) Values in Predicting the Severity of Disease

OBJECTIVES

Advanced molecular diagnostic methods like real-time polymerase chain reaction (PCR) play a vital role in the early recognition of viral infections, including the coronavirus disease 2019 (COVID-19). Therefore, in the context of the recent COVID-19 pandemic, this study aimed to determine the correlation of cycle threshold (Ct) values with symptoms in COVID-19-positive patients.

MATERIALS AND METHODS

A retrospective study was conducted in a virus research diagnostic laboratory (VRDL) at a COVID-19-dedicated tertiary care hospital in South India. A total of 5563 COVID-19-positive patients were analyzed for symptom spectrum and duration of illness with Ct values of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

RESULTS

Around 80% (n= 4401) of the patients were symptomatic and the rest were asymptomatic. Among the symptomatic patients, fever (66%) was the most common symptom. About 44% of symptomatic patients had a low Ct value (Ct ≤ 24). There was a significant difference in symptoms among patients with low, medium, and high Ct values. In the subpopulation of symptomatic patients analyzed for the association of Ct value and duration of illness, the mean duration of illness was three days and almost 88% of the patients were tested within five days of onset of symptoms. It has been observed that a shorter duration of illness lowers the Ct values. A significant association was seen between the mean Ct value and days since symptom onset (p-value = 0.016).

CONCLUSION

Most of the symptomatic patients had lower Ct values in comparison to the asymptomatic patients. A significant association between low Ct values and the duration of symptoms observed in our study explains the viral dynamics, i.e., higher viral shedding at the onset of symptoms and declines thereafter.

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

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 (Rt) 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.

Can the nucleic acid Ct value of discharged patients infected with SARS-CoV-2 Omicron variant be 35?--A retrospective study on fluctuation of nucleic acid Ct values in SNIEC mobile cabin hospital

Objective

To explore the meaning of cycle threshold (Ct) value fluctuation and the appropriateness of setting the discharge Ct value to 35, which is the current standard in Chinese guidelines.

Method

A retrospective study was conducted on 95 patients with Ct value fluctuation (Ct value below 35 on day 3; group A) and 97 patients with a normal discharge process (control; group B). Their clinical characteristics and follow-up data were collected.

Results

(1) There was no significant difference between the groups in age, gender distribution, number of vaccinations, initial ORF-Ct value, and initial N-Ct value. The proportion of patients complicated with chronic internal disorders, respiratory symptoms, and abnormal chest radiology in group A was significantly higher than that in group B. (2) Between the two groups, there was no significant difference in the ORF-Ct or N-Ct value on day 1, but the ORF-Ct and N-Ct values of group B on days 2 to 4 were significantly higher than those of group A. (3) There was no significant difference between the groups in the ORF-Ct value at discharge, but there was a significant difference in the N-Ct value at discharge. Seven days after discharge, almost 100% of the patients had been cured. The mean negative conversion interval of nucleic acid of the patients in group A was 14.5 ± 4.6 days, which was longer than that of the patients in group B (11.8 ± 4 days). (4) Logistic regression analysis showed that the ORF-Ct value on day 2 was the key factor influencing the Ct value fluctuation.

Conclusion

The fluctuation of Ct value is only a normal phenomenon in the recovery period of the disease, and there is no need for excessive intervention. It is reasonable to set the Ct value of the discharge standard to 35 and retest the nucleic acid on the 10^th day after discharge for patients with underlying diseases or symptoms.

Weekly Nowcasting of New COVID-19 Cases Using Past Viral Load Measurements

The rapid spread of the coronavirus disease COVID-19 has imposed clinical and financial burdens on hospitals and governments attempting to provide patients with medical care and implement disease-controlling policies. The transmissibility of the disease was shown to be correlated with the patient’s viral load, which can be measured during testing using the cycle threshold (Ct). Previous models have utilized Ct to forecast the trajectory of the spread, which can provide valuable information to better allocate resources and change policies. However, these models combined other variables specific to medical institutions or came in the form of compartmental models that rely on epidemiological assumptions, all of which could impose prediction uncertainties. In this study, we overcome these limitations using data-driven modeling that utilizes Ct and previous number of cases, two institution-independent variables. We collected three groups of patients (n = 6296, n = 3228, and n = 12,096) from different time periods to train, validate, and independently validate the models. We used three machine learning algorithms and three deep learning algorithms that can model the temporal dynamic behavior of the number of cases. The endpoint was 7-week forward number of cases, and the prediction was evaluated using mean square error (MSE). The sequence-to-sequence model showed the best prediction during validation (MSE = 0.025), while polynomial regression (OLS) and support vector machine regression (SVR) had better performance during independent validation (MSE = 0.1596, and MSE = 0.16754, respectively), which exhibited better generalizability of the latter. The OLS and SVR models were used on a dataset from an external institution and showed promise in predicting COVID-19 incidences across institutions. These models may support clinical and logistic decision-making after prospective validation.

SARS-CoV-2 Droplet and Airborne Transmission Heterogeneity

The spread dynamics of the SARS-CoV-2 virus have not yet been fully understood after two years of the pandemic. The virus's global spread represented a unique scenario for advancing infectious disease research. Consequently, mechanistic epidemiological theories were quickly dismissed, and more attention was paid to other approaches that considered heterogeneity in the spread. One of the most critical advances in aerial pathogens transmission was the global acceptance of the airborne model, where the airway is presented as the epicenter of the spread of the disease. Although the aerodynamics and persistence of the SARS-CoV-2 virus in the air have been extensively studied, the actual probability of contagion is still unknown. In this work, the individual heterogeneity in the transmission of 22 patients infected with COVID-19 was analyzed by close contact (cough samples) and air (environmental samples). Viral RNA was detected in 2/19 cough samples from patient subgroups, with a mean Ct (Cycle Threshold in Quantitative Polymerase Chain Reaction analysis) of 25.7 ± 7.0. Nevertheless, viral RNA was only detected in air samples from 1/8 patients, with an average Ct of 25.0 ± 4.0. Viral load in cough samples ranged from 7.3 × 105 to 8.7 × 108 copies/mL among patients, while concentrations between 1.1-4.8 copies/m3 were found in air, consistent with other reports in the literature. In patients undergoing follow-up, no viral load was found (neither in coughs nor in the air) after the third day of symptoms, which could help define quarantine periods in infected individuals. In addition, it was found that the patient's Ct should not be considered an indicator of infectiousness, since it could not be correlated with the viral load disseminated. The results of this work are in line with proposed hypotheses of superspreaders, which can attribute part of the heterogeneity of the spread to the oversized emission of a small percentage of infected people.