Trends in SARS-CoV-2 cycle threshold values in the Czech Republic from April 2020 to April 2022

Dita Musalkova,Lenka Piherova, Ondrej Kwasny, Zuzana Dindova, Lubor Stancik,Hana Hartmannova, Otomar Slama, Petra Peckova, Josef Pargac,Gabriel Minarik, Tomas Zima,Anthony J. Bleyer, Martin Radina,Michal Pohludka,Stanislav Kmoch

Scientific Reports(2023)

引用 1|浏览3
暂无评分
摘要
The inability to predict the evolution of the COVID-19 epidemic hampered abilities to respond to the crisis effectively. The cycle threshold (Ct) from the standard SARS-CoV-2 quantitative reverse transcription-PCR (RT-qPCR) clinical assay is inversely proportional to the amount of SARS-CoV-2 RNA in the sample. We were interested to see if population Ct values could predict future increases in COVID-19 cases as well as subgroups that would be more likely to be affected. This information would have been extremely helpful early in the COVID-19 epidemic. We therefore conducted a retrospective analysis of demographic data and Ct values from 2,076,887 nasopharyngeal swab RT-qPCR tests that were performed at a single diagnostic laboratory in the Czech Republic from April 2020 to April 2022 and from 221,671 tests that were performed as a part of a mandatory school surveillance testing program from March 2021 to March 2022. We found that Ct values could be helpful predictive tools in the real-time management of viral epidemics. First, early measurement of Ct values would have indicated the low viral load in children, equivalent viral load in males and females, and higher viral load in older individuals. Second, rising or falling median Ct values and differences in Ct distribution indicated changes in the transmission in the population. Third, monitoring Ct values and positivity rates would have provided early evidence as to whether prevention measures are effective. Health system authorities should thus consider collecting weekly median Ct values of positively tested samples from major diagnostic laboratories for regional epidemic surveillance.
更多
查看译文
关键词
Diseases,Epidemiology,Respiratory tract diseases,Science,Humanities and Social Sciences,multidisciplinary
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要