Viruses (Jan 2025)

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

  • Judith Carolina De Arcos-Jiménez,
  • Ernestina Quintero-Salgado,
  • Pedro Martínez-Ayala,
  • Gustavo Rosales-Chávez,
  • Roberto Miguel Damian-Negrete,
  • Oscar Francisco Fernández-Diaz,
  • Mariana del Rocio Ruiz-Briseño,
  • Rosendo López-Romo,
  • Patricia Noemi Vargas-Becerra,
  • Ruth Rodríguez-Montaño,
  • Ana María López-Yáñez,
  • Jaime Briseno-Ramirez

DOI
https://doi.org/10.3390/v17010103
Journal volume & issue
Vol. 17, no. 1
p. 103

Abstract

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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.

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