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DOI	10.1126/science.abh0635
	Estimating epidemiologic dynamics from cross-sectional viral load distributions
	Hay J.A.; Kennedy-Shaffer L.; Kanjilal S.; Lennon N.J.; Gabriel S.B.; Lipsitch M.; Mina M.J.
发表日期	2021
ISSN	0036-8075
卷号	373 期号:6552
英文摘要	Estimating an epidemic’s trajectory is crucial for developing public health responses to infectious diseases, but case data used for such estimation are confounded by variable testing practices. We show that the population distribution of viral loads observed under random or symptom-based surveillance—in the form of cycle threshold (Ct) values obtained from reverse transcription quantitative polymerase chain reaction testing—changes during an epidemic. Thus, Ct values from even limited numbers of random samples can provide improved estimates of an epidemic’s trajectory. Combining data from multiple such samples improves the precision and robustness of this estimation. We apply our methods to Ct values from surveillance conducted during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in a variety of settings and offer alternative approaches for real-time estimates of epidemic trajectories for outbreak management and response. © 2021 American Association for the Advancement of Science. All rights reserved.
英文关键词	COVID-19; epidemic; polymerase chain reaction; public health; severe acute respiratory syndrome; trajectory; Article; cohort analysis; coronavirus disease 2019; cycle threshold value; disease course; disease surveillance; health care management; human; human tissue; incidence; mathematical model; pandemic; population distribution; real time reverse transcription polymerase chain reaction; Severe acute respiratory syndrome coronavirus 2; virus load; cross-sectional study; diagnosis; epidemiological monitoring; epidemiology; incidence; physiology; theoretical model; virology; SARS coronavirus; COVID-19; COVID-19 Nucleic Acid Testing; Cross-Sectional Studies; Epidemiological Monitoring; Humans; Incidence; Models, Theoretical; Pandemics; SARS-CoV-2; Viral Load
语种	英语
来源期刊	Science
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/243977
作者单位	Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Mathematics and Statistics, Vassar College, Poughkeepsie, NY, United States; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA, United States; Department of Infectious Diseases, Brigham and Women’s Hospital, Boston, MA, United States; Broad Institute of MIT and Harvard, Cambridge, MA, United States; Department of Pathology, Brigham and Women’s Hospital, Boston, MA, United States
推荐引用方式 GB/T 7714	Hay J.A.,Kennedy-Shaffer L.,Kanjilal S.,et al. Estimating epidemiologic dynamics from cross-sectional viral load distributions[J],2021,373(6552).
APA	Hay J.A..,Kennedy-Shaffer L..,Kanjilal S..,Lennon N.J..,Gabriel S.B..,...&Mina M.J..(2021).Estimating epidemiologic dynamics from cross-sectional viral load distributions.Science,373(6552).
MLA	Hay J.A.,et al."Estimating epidemiologic dynamics from cross-sectional viral load distributions".Science 373.6552(2021).