Investigation of the Relationship between Population SARS-CoV-2 Cycle Threshold Values and the Trend of COVID-19 Infection: A longitudinal study

Paria Dehesh, Hamid Reza Baradaran, Babak Eshrati, Seyed Abbas Motevalian, Masoud Salehi, Tahereh Donyavi

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Abstract

BACKGROUND: Distribution of population-level cycle threshold (Ct) values as a proxy of viral load may be useful indicators for predicting COVID-19 dynamics. .

OBJECTIVE: This study aimed to determine the relationship between the daily trend of average Ct value and the COVID-19 dynamics, which were done as the daily number of hospitalized patients with COVID-19, the new daily number of positive tests, the daily number of COVID-19 death, and the number of hospitalized patients with COVID-19 by age, and also, to determine the lag between these series.

METHODS: The samples included in this study were collected from March 21, 2021, to December 1, 2021. Daily Ct values of all patients who were referred to the Molecular Diagnostic Laboratory of Iran University of Medical Sciences in Tehran, Iran, for RT-PCR tests were recorded. The daily number of positive people and the number of hospitalized people by age group were extracted from the COVID-19 patient information registration system in Tehran province, Iran. First, the ARIMA model was done to the time series of variables. Second, cross-correlation analysis was done to determine the best lag and correlation between the average daily Ct value and other COVID-19 dynamics variables. Finally, the best-selected lag of Ct through cross-correlation was incorporated as covariates into the ARIMAX model to find the coefficient.

RESULTS: Daily average Ct values have a significant negative correlation (a 23-day time delay) with the new daily number of hospitalized patients (P=0.02), 30- day time delay with the new daily number of positive tests ( p=0.015), and a daily number of COVID-19 death (P=0.016). Daily average Ct value with a 30-day delay could impact the daily number of the positive test for COVID-19 (β =-16.87, P <0.0001) and the daily number of death from COVID-19 (β= -1.52, P=0.026). There is a significant coefficient between Ct lag (23 days) and the number of COVID 19 hospitalization (β =-24.12, P=0.005). Cross-correlation analysis showed significant time delays in the average Ct values and the number of daily hospitalized patients 18-59(a 23-day time delay, P=0.019) and over 60 years old(a 23-day time delay, P=<0.0001). No statistically significant relation was detected in the number of daily hospitalized patients under five years(a 9-day time delay, P=0.270) and patients aged 5-17 years(a 13-day time delay, P=0.389).

CONCLUSIONS: It is important for surveillance of COVID-19 disease to find a good indicator that can predict epidemic surges in the community. It seems that the average daily Ct value with a difference of 30 days delay can predict increases in the number of positive confirmed COVID-19 cases so it may be a useful indicator for the health system.

Original languageEnglish
Article numbere36424
Number of pages12
JournalJMIR Public Health and Surveillance
Volume8
Issue number11
DOIs
Publication statusPublished - 8 Nov 2022

Keywords

  • cycle threshold value
  • COVID-19
  • surveillance
  • trend
  • epidemiology
  • disease surveillance
  • digital surveillance
  • prediction model
  • epidemic modeling
  • health system
  • infectious disease

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