Different dynamics of mean SARS‐CoV‐2 RT‐PCR Ct values between the first and second COVID‐19 waves in the Madrid population

Rodríguez‐Grande, Cristina; Catalán, Pilar; Alcalá, Luís; Buenestado‐Serrano, Sergio; Adán‐Jiménez, Javier; Rodríguez‐Maus, Sandra; Herránz, Marta; Sicilia, Jon; Acosta, Francisco J.; Pérez‐Lago, Laura; Muñóz, Patricia; Viedma, Darío García de

doi:10.1111/tbed.14045

Cited by 13 publications

(9 citation statements)

References 10 publications

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“…A good number of previous studies have shown that CT values, either taken at a given time or analyzing its temporal evolution, may help predict the posterior occurrence of new incident cases or hospitalizations caused by the SARS-CoV-2 infection [ 11 , 17 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. To our knowledge, this is the first study to investigate the utility of measuring CT values to predict the evolution of the pandemic using the Granger causality approach, i.e., an analysis of temporal series.…”

Section: Discussionmentioning

confidence: 99%

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

Cava

Román

Barreiro

et al. 2023

Viruses

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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.

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Section: Discussionmentioning

confidence: 99%

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

Cava

Román

Barreiro

et al. 2023

Viruses

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“…This is consistent with observations of decreasing Ct values during COVID-19 surges in other studies and suggests that aggregate Ct values may provide an indirect indication of the overall viral load in the exposure environment at a population level. [18] , [19] , [20] …”

Section: Discussionmentioning

confidence: 99%

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

Phillips

Quintero

Wald-Dickler

et al. 2022

Journal of Clinical Virology

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“…Ct values were proposed to have potential prognostic value in predicting severity, infectiousness, and mortality among patients [ 15 ]. Ct values were also used to determine the duration an infected patient needs to quarantine [ 16 , 17 ]. A high Ct value (indicating a low viral load) is detected at the early stages of the infection before the person becomes contagious and at the late stages when the risk of transmission is low [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

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

Khalil

Handawi

Mohsen

et al. 2022

Viruses

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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.

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Different dynamics of mean SARS‐CoV‐2 RT‐PCR Ct values between the first and second COVID‐19 waves in the Madrid population

Cited by 13 publications

References 10 publications

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

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

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

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

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