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

<h4>Background</h4> 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 p...

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Main Authors: Naila Shoaib, Asim Iqbal, Farhad Ali Shah, Wajeeha Zainab, Maham Qasim, Noore Zerqoon, Muhammad Omer Naseem, Rimsha Munir, Nousheen Zaidi
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2023-01-01
Series:PLoS ONE
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9997994/?tool=EBI
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author Naila Shoaib
Asim Iqbal
Farhad Ali Shah
Wajeeha Zainab
Maham Qasim
Noore Zerqoon
Muhammad Omer Naseem
Rimsha Munir
Nousheen Zaidi
author_facet Naila Shoaib
Asim Iqbal
Farhad Ali Shah
Wajeeha Zainab
Maham Qasim
Noore Zerqoon
Muhammad Omer Naseem
Rimsha Munir
Nousheen Zaidi
author_sort Naila Shoaib
collection DOAJ
description <h4>Background</h4> 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. <h4>Methods</h4> 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. <h4>Results</h4> 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. <h4>Conclusions</h4> Decreasing population-level median Ct values for asymptomatic COVID-19 cases appear to be a leading indicator for predicting future COVID-19 cases.
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spelling doaj.art-9f1e2648003341809d54a066fc4af25d2023-03-12T05:32:20ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01183Population-level median cycle threshold (Ct) values for asymptomatic COVID-19 cases can predict the trajectory of future casesNaila ShoaibAsim IqbalFarhad Ali ShahWajeeha ZainabMaham QasimNoore ZerqoonMuhammad Omer NaseemRimsha MunirNousheen Zaidi<h4>Background</h4> 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. <h4>Methods</h4> 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. <h4>Results</h4> 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. <h4>Conclusions</h4> Decreasing population-level median Ct values for asymptomatic COVID-19 cases appear to be a leading indicator for predicting future COVID-19 cases.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9997994/?tool=EBI
spellingShingle Naila Shoaib
Asim Iqbal
Farhad Ali Shah
Wajeeha Zainab
Maham Qasim
Noore Zerqoon
Muhammad Omer Naseem
Rimsha Munir
Nousheen Zaidi
Population-level median cycle threshold (Ct) values for asymptomatic COVID-19 cases can predict the trajectory of future cases
PLoS ONE
title Population-level median cycle threshold (Ct) values for asymptomatic COVID-19 cases can predict the trajectory of future cases
title_full Population-level median cycle threshold (Ct) values for asymptomatic COVID-19 cases can predict the trajectory of future cases
title_fullStr Population-level median cycle threshold (Ct) values for asymptomatic COVID-19 cases can predict the trajectory of future cases
title_full_unstemmed Population-level median cycle threshold (Ct) values for asymptomatic COVID-19 cases can predict the trajectory of future cases
title_short Population-level median cycle threshold (Ct) values for asymptomatic COVID-19 cases can predict the trajectory of future cases
title_sort population level median cycle threshold ct values for asymptomatic covid 19 cases can predict the trajectory of future cases
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9997994/?tool=EBI
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