SARS-CoV-2 cycle threshold (Ct) values predict future COVID-19 cases

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.

Estimating the time-varying effective reproduction number via Cycle Threshold-based Transformer

Monitoring the spread of infectious disease is essential to design and adjust the interventions timely for the prevention of the epidemic outbreak and safeguarding the public health. The governments have generally adopted the incidence-based statistical method to estimate the time-varying effective reproduction number Rt and evaluate the transmission ability of epidemics. However, this method exhibits biases arising from the reported incidence data and assumes the generation interval distribution which is not available at the early stage of epidemic. Recent studies showed that the viral loads characterized by cycle threshold (Ct) of the infected populations evolving throughout the course of epidemic and providing a possibility to infer the epidemic trajectory. In this work, we propose the Cycle Threshold-based Transformer (Ct-Transformer) to estimate Rt. We find the supervised learning of Ct-Transformer outperforms the traditional incidence-based statistic and Ct-based Rt estimating methods, and more importantly Ct-Transformer is robust to the detection resources. Further, we apply the proposed model to self-supervised pre-training tasks and obtain excellent fine-tuned performance, which attains comparable performance with the supervised Ct-Transformer, verified by both the synthetic and real-world datasets. We demonstrate that the Ct-based deep learning method can improve the real-time estimates of Rt, enabling more easily adapted to the track of the newly emerged epidemic.

Using intrahost single nucleotide variant data to predict SARS-CoV-2 detection cycle threshold values

Over the last four years, each successive wave of the COVID-19 pandemic has been caused by variants with mutations that improve the transmissibility of the virus. Despite this, we still lack tools for predicting clinically important features of the virus. In this study, we show that it is possible to predict the PCR cycle threshold (Ct) values from clinical detection assays using sequence data. Ct values often correspond with patient viral load and the epidemiological trajectory of the pandemic. Using a collection of 36,335 high quality genomes, we built models from SARS-CoV-2 intrahost single nucleotide variant (iSNV) data, computing XGBoost models from the frequencies of A, T, G, C, insertions, and deletions at each position relative to the Wuhan-Hu-1 reference genome. Our best model had an R2 of 0.604 [0.593-0.616, 95% confidence interval] and a Root Mean Square Error (RMSE) of 5.247 [5.156-5.337], demonstrating modest predictive power. Overall, we show that the results are stable relative to an external holdout set of genomes selected from SRA and are robust to patient status and the detection instruments that were used. This study highlights the importance of developing modeling strategies that can be applied to publicly available genome sequence data for use in disease prevention and control.

Cycle Threshold Values of SARS-CoV-2 RT-PCR during Outbreaks in Nursing Homes: A Retrospective Cohort Study

Backgound/Objectives: Cycle threshold (Ct) values of SARS-CoV-2 real-time reverse transcriptase-polymerase chain reaction (RT-PCR) tests are associated with infectivity and viral load, and they could be an aid in forecasting the evolution of SARS-CoV-2 outbreaks. The objective was to know the Ct values related to the incidence and reinfection of SARS-CoV-2 in successive outbreaks, which took place in nursing homes in Castellon (Spain) during 2020-2022, and to test its usefulness as an instrument of epidemic surveillance in nursing homes.

METHODS

a retrospective cohort design with Poisson regression and multinomial logistic regression were used.

RESULTS

We studied four nursing home SARS-CoV-2 outbreaks, and the average infection rate, reinfection rate, and case fatality were 72.7%, 19.9%, and 5.5%, respectively; 98.9% of residents were vaccinated with three doses of a mRNA SARS-CoV-2 vaccine. Ct values for first infections and reinfections were 27.1 ± 6.6 and 31.9 ± 5.4 (p = 0.000). Considering Ct values ≥ 30 versus <30, residents with reinfections had Ct values higher than residents with a first infection, an adjusted relative risk of 1.66 (95% Confidence interval 1.10-2.51). A sensitivity analysis confirmed these results.

CONCLUSIONS

Reinfection and SARS-CoV-2 vaccination (hybrid immunity) could protect against severe disease better than vaccination alone. High Ct values suggest lower transmission and severity. Its value can be useful for surveillance and forecasting future SARS-CoV-2 epidemics.

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.

Temporal Series Analysis of Population Cycle Threshold Counts as a Predictor of Surge in Cases and Hospitalizations during the SARS-CoV-2 Pandemic

Tools to predict surges in cases and hospitalizations during the COVID-19 pandemic may help guide public health decisions. Low cycle threshold (CT) counts may indicate greater SARS-CoV-2 concentrations in the respiratory tract, and thereby may be used as a surrogate marker of enhanced viral transmission. Several population studies have found an association between the oscillations in the mean CT over time and the evolution of the pandemic. For the first time, we applied temporal series analysis (Granger-type causality) to validate the CT counts as an epidemiological marker of forthcoming pandemic waves using samples and analyzing cases and hospital admissions during the third pandemic wave (October 2020 to May 2021) in Madrid. A total of 22,906 SARS-CoV-2 RT-PCR-positive nasopharyngeal swabs were evaluated; the mean CT value was 27.4 (SD: 2.1) (22.2% below 20 cycles). During this period, 422,110 cases and 36,727 hospital admissions were also recorded. A temporal association was found between the CT counts and the cases of COVID-19 with a lag of 9-10 days (p ≤ 0.01) and hospital admissions by COVID-19 (p < 0.04) with a lag of 2-6 days. According to a validated method to prove associations between variables that change over time, the short-term evolution of average CT counts in the population may forecast the evolution of the COVID-19 pandemic.

Positivization time of a COVID-19 rapid antigen self-test predicts SARS-CoV-2 viral load: a proof of concept

OBJECTIVES

This proof of concept study was aimed to validate the hypothesis that the time of positivization of SARS-CoV-2 self-performed rapid diagnostic tests (RDTs) may reflect the actual viral load in the specimen.

METHODS

A SARS-CoV-2 positive sample with high viral load was diluted and concomitantly assayed with molecular assay (Xpert Xpress SARS-CoV-2) and RDT (COVID-VIRO ALL IN RDT). The (mean cycle threshold; Ct) values and RDT positivization times of these dilutions were plotted and interpolated by calculating the best fit. The parameters of this equation were then used for converting the positivization times into RDT-estimated SARS-CoV-2 Ct values in routine patient samples.

RESULTS

The best fit between measured and RDT-estimated Ct values could be achieved with a 2-degree polynomial curve. The RDT-estimated Ct values exhibited high correlation (r=0.996) and excellent Deming fit (y=1.01 × x - 0.18) with measured Ct values. In 30 consecutive patients with positive RDT test, the correlation between RDT positivization time and measured Ct value was r=0.522 (p=0.003). The correlation of RDT-estimated and measured Ct values slightly improved to 0.577 (Deming fit: y=0.44 × x + 11.08), displaying a negligible bias (1.0; 95% CI, -0.2 to 2.2; p=0.105). Concordance of RDT-estimated and measured Ct values at the <20 cut-off was 80%, with 0.84 sensitivity and 0.73 specificity.

CONCLUSIONS

This proof of concept study demonstrates the potential feasibility of using RDTs for garnering information on viral load in patients with acute SARS-CoV-2 infection.

Population-level median cycle threshold (Ct) values for asymptomatic COVID-19 cases can predict the trajectory of future cases

BACKGROUND

Recent studies indicate that the population-level SARS-CoV-2 cycle threshold (Ct) values can inform the trajectory of the pandemic. The presented study investigates the potential of Ct values in predicting the future of COVID-19 cases. We also determined whether the presence of symptoms could change the correlation between Ct values and future cases.

METHODS

We examined the individuals (n = 8660) that consulted different sample collection points of a private diagnostic center in Pakistan for COVID-19 testing between June 2020 and December 2021. The medical assistant collected clinical and demographic information. The nasopharyngeal swab specimens were taken from the study participants and real-time reverse transcriptase polymerase chain reaction (RT-PCR) was used to detect SARS-CoV-2 in these samples.

RESULTS

We observed that median Ct values display significant temporal variations, which show an inverse relationship with future cases. The monthly overall median Ct values negatively correlated with the number of cases occurring one month after specimen collection (r = -0.588, p <0.05). When separately analyzed, Ct values for symptomatic cases displayed a weak negative correlation (r = -0.167, p<0.05), while Ct values from asymptomatic cases displayed a stronger negative correlation (r = -0.598, p<0.05) with the number of cases in the subsequent months. Predictive modeling using these Ct values closely forecasted the increase or decrease in the number of cases of the subsequent month.

CONCLUSIONS

Decreasing population-level median Ct values for asymptomatic COVID-19 cases appear to be a leading indicator for predicting future COVID-19 cases.

Bimodal distribution pattern associated with the PCR cycle threshold (Ct) and implications in COVID-19 infections

SARS-CoV-2 is notable for its extremely high level of viral replication in respiratory epithelial cells, relative to other cell types. This may partially explain the high transmissibility and rapid global dissemination observed during the COVID-19 pandemic. Polymerase chain reaction (PCR) cycle threshold (Ct) number has been widely used as a proxy for viral load based on the inverse relationship between Ct number and amplifiable genome copies present in a sample. We examined two PCR platforms (Centers for Disease Control and Prevention 2019-nCoV Real-time RT-PCR, Integrated DNA Technologies; and TaqPath COVID-19 multi-plex combination kit, ThermoFisher Scientific) for their performance characteristics and Ct distribution patterns based on results generated from 208,947 clinical samples obtained between October 2020 and September 2021. From 14,231 positive tests, Ct values ranged from 8 to 39 and displayed a pronounced bimodal distribution. The bimodal distribution persisted when stratified by gender, age, and time period of sample collection during which different viral variants circulated. This finding may be a result of heterogeneity in disease progression or host response to infection irrespective of age, gender, or viral variants. Quantification of respiratory mucosal viral load may provide additional insight into transmission and clinical indicators helpful for infection control.