Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/35810
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dc.contributor.authorYin, Nicolas-
dc.contributor.authorDellicour, Simon-
dc.contributor.authorDaubie, Valery-
dc.contributor.authorFRANCO, Nicolas-
dc.contributor.authorWautier, Magali-
dc.contributor.authorFAES, Christel-
dc.contributor.authorVan Cauteren, Dieter-
dc.contributor.authorNymark, Liv-
dc.contributor.authorHENS, Niel-
dc.contributor.authorGilbert, Marius-
dc.contributor.authorHallin, Marie-
dc.contributor.authorVandenberg, Olivier-
dc.date.accessioned2021-11-16T10:23:26Z-
dc.date.available2021-11-16T10:23:26Z-
dc.date.issued2021-
dc.date.submitted2021-11-02T15:12:08Z-
dc.identifier.citationFrontiers in medicine (Lausanne), 8 (Art N° 743988)-
dc.identifier.urihttp://hdl.handle.net/1942/35810-
dc.description.abstractIntroduction: We 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 epidemic’s dynamics at local and national levels and for improving forecasting models. Methods: SARS-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. Results: Over 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. Conclusion: We provide preliminary evidence that trends of Ct values can help to both follow and predict the epidemic’s 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.-
dc.description.sponsorshipFUNDING SD was supported by the Fonds National de la Recherche Scientifique (FNRS, Belgium). NF, CF, and NH acknowledge support from the European Union’s Horizon 2020 research and innovation program (NF and NH: Grant Number: 682540—TransMID project; CF and NH: Grant Number: 101003688—EpiPose project). ACKNOWLEDGMENTS We wish to thank the personnel of the LHUB-ULB for its daily technical assistance. The authors would like to thank Dirk Thielens for his appreciable assistance. This work is dedicated to the healthcare workers, the patients, and families affected by SARS-CoV-2.-
dc.language.isoen-
dc.publisher-
dc.rights2021 Yin, Dellicour, Daubie, Franco, Wautier, Faes, Van Cauteren, Nymark, Hens, Gilbert, Hallin and Vandenberg. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with thes terms.-
dc.subject.otherCOVID-19-
dc.subject.otherSARS-CoV-2-
dc.subject.otherforecast-
dc.subject.otherepidemic trend-
dc.subject.othercycle threshold (Ct) value-
dc.titleLeveraging of SARS-CoV-2 PCR Cycle Thresholds Values to Forecast COVID-19 Trends-
dc.typeJournal Contribution-
dc.identifier.volume8-
local.bibliographicCitation.jcatA1-
local.publisher.placeAVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND-
local.type.refereedRefereed-
local.type.specifiedArticle-
local.bibliographicCitation.artnr743988-
local.type.programmeH2020-
local.relation.h2020682540-
dc.identifier.doi10.3389/fmed.2021.743988-
dc.identifier.isi000719999700001-
dc.identifier.eissn2296-858X-
local.provider.typeCrossRef-
local.uhasselt.uhpubyes-
local.uhasselt.internationalyes-
item.validationecoom 2022-
item.contributorYin, Nicolas-
item.contributorDellicour, Simon-
item.contributorDaubie, Valery-
item.contributorFRANCO, Nicolas-
item.contributorWautier, Magali-
item.contributorFAES, Christel-
item.contributorVan Cauteren, Dieter-
item.contributorNymark, Liv-
item.contributorHENS, Niel-
item.contributorGilbert, Marius-
item.contributorHallin, Marie-
item.contributorVandenberg, Olivier-
item.fullcitationYin, Nicolas; Dellicour, Simon; Daubie, Valery; FRANCO, Nicolas; Wautier, Magali; FAES, Christel; Van Cauteren, Dieter; Nymark, Liv; HENS, Niel; Gilbert, Marius; Hallin, Marie & Vandenberg, Olivier (2021) Leveraging of SARS-CoV-2 PCR Cycle Thresholds Values to Forecast COVID-19 Trends. In: Frontiers in medicine (Lausanne), 8 (Art N° 743988).-
item.fulltextWith Fulltext-
item.accessRightsOpen Access-
crisitem.journal.eissn2296-858X-
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