Viruses (Feb 2023)

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

  • Fernando Cava,
  • Jesús San Román,
  • Pablo Barreiro,
  • Francisco Javier Candel,
  • Francisco Javier Álvarez-Timón,
  • David Melero,
  • Nerea Coya,
  • Raquel Guillén,
  • David Cantarero-Prieto,
  • Javier Lera-Torres,
  • Noelia Cobo-Ortiz,
  • Jesús Canora,
  • Francisco Javier Martínez-Peromingo,
  • Raquel Barba,
  • María del Mar Carretero,
  • Juan Emilio Losa,
  • Antonio Zapatero

DOI
https://doi.org/10.3390/v15020421
Journal volume & issue
Vol. 15, no. 2
p. 421

Abstract

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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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