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Leveraging of SARS-CoV-2 PCR cycle thresholds values (Ct) to forecast COVID-19 trends
Nicolas Yin; Simon Dellicour; Valery Daubie; Nicolas Franco; Magali Wautier; Christel Faes; Dieter Van Cauteren; Liv Nymark; Niel Hens; Marius Gilbert; Marie Hallin; Olivier Vandenberg.
Afiliación
  • Nicolas Yin; Department of Microbiology, Laboratoire Hospitalier Universtaire de Bruxelles - Universitair Laboratorium Brussel (LHUB-ULB), Université Libre de Bruxelles (ULB
  • Simon Dellicour; Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
  • Valery Daubie; Department of Microbiology, Laboratoire Hospitalier Universtaire de Bruxelles - Universitair Laboratorium Brussel (LHUB-ULB), Université Libre de Bruxelles (ULB
  • Nicolas Franco; Namur Centre for Complex Systems (naXys) & Department of Mathematics, University of Namur, Namur, Belgium
  • Magali Wautier; Department of Microbiology, Laboratoire Hospitalier Universtaire de Bruxelles - Universitair Laboratorium Brussel (LHUB-ULB), Université Libre de Bruxelles (ULB
  • Christel Faes; Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University (UHasselt), Hasselt, Belgium
  • Dieter Van Cauteren; Scientific Directorate of Epidemiology and public health, Sciensano, Brussels, Belgium
  • Liv Nymark; Norwegian Institute of Public Health, Division of Infection Control and Environmental Health, Oslo, Norway
  • Niel Hens; Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University (UHasselt), Hasselt, Belgium
  • Marius Gilbert; Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
  • Marie Hallin; Department of Microbiology, Laboratoire Hospitalier Universtaire de Bruxelles - Universitair Laboratorium Brussel (LHUB-ULB), Université Libre de Bruxelles (ULB
  • Olivier Vandenberg; Clinical Research and Innovation Unit, Laboratoire Hospitalier Universtaire de Bruxelles - Universitair Laboratorium Brussel (LHUB-ULB), Université Libre de Bru
Preprint en Inglés | medRxiv | ID: ppmedrxiv-21260679
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ABSTRACT
IntroductionWe assessed the usefulness of SARS-CoV-2 RT-PCR cycle thresholds (Ct) values trends produced by the LHUB-ULB (a consolidated microbiology laboratory located in Brussels, Belgium) for monitoring the epidemics dynamics at local and national levels and for improving forecasting models. MethodsSARS-CoV-2 RT-PCR Ct values produced from April 1, 2020, to May 15, 2021, were compared with national COVID-19 confirmed cases notifications according to their geographical and time distribution. These Ct values were evaluated against both a phase diagram predicting the number of COVID-19 patients requiring intensive care and an age-structured model estimating COVID-19 prevalence in Belgium. ResultsOver 155,811 RT-PCR performed, 12,799 were positive and 7,910 Ct values were available for analysis. The 14-day median Ct values were negatively correlated with the 14-day mean daily positive tests with a lag of 17 days. In addition, the 14-day mean daily positive tests in LHUB-ULB were strongly correlated with the 14-day mean confirmed cases in the Brussels-Capital and in Belgium with coinciding start, peak and end of the different waves of the epidemic. Ct values decreased concurrently with the forecasted phase-shifts of the diagram. Similarly, the evolution of 14-day median Ct values was negatively correlated with daily estimated prevalence for all age-classes. ConclusionWe provide preliminary evidence that trends of Ct values can help to both follow and predict the epidemics trajectory at local and national levels, underlining that consolidated microbiology laboratories can act as epidemic sensors as they gather data that are representative of the geographical area they serve.
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Texto completo: Disponible Colección: Preprints Base de datos: medRxiv Tipo de estudio: Experimental_studies / Estudio observacional / Estudio pronóstico Idioma: Inglés Año: 2021 Tipo del documento: Preprint
Texto completo: Disponible Colección: Preprints Base de datos: medRxiv Tipo de estudio: Experimental_studies / Estudio observacional / Estudio pronóstico Idioma: Inglés Año: 2021 Tipo del documento: Preprint
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