The incubation period of COVID-19 – A rapid systematic review and meta-analysis of observational research

Author:

McAloon Conor G.ORCID,Collins Áine B.,Hunt Kevin,Barber Ann,Byrne Andrew W.ORCID,Butler Francis,Casey Miriam,Griffin John,Lane Elizabeth,McEvoy David,Wall Patrick,Green Martin J.,O’Grady Luke,More Simon J.

Abstract

ABSTRACTBackgroundReliable estimates of the incubation period are important for decision making around the control of infectious diseases. Knowledge of the incubation period distribution can be used directly to inform decision-making or as inputs into mathematical models.ObjectivesThe aim of this study was to conduct a rapid systematic review and meta-analysis of estimates of the incubation periods of COVID-19.DesignRapid systematic review and meta-analysis of observational researchData sourcesPublications on the electronic databases PubMed, Google Scholar, MedRxiv and BioRxiv were searched. The search was not limited to peer-reviewed published data, but also included pre-print articles.Study appraisal and synthesis methodsStudies were selected for meta-analysis if they reported either the parameters and confidence intervals of the distributions fit to the data, or sufficient information to facilitate calculation of those values. The majority of studies suitable for inclusion in the final analysis modelled incubation period as a lognormal distribution. We conducted a random effects meta-analysis of the parameters of this distribution.ResultsThe incubation period distribution may be modelled with a lognormal distribution with pooled mu and sigma parameters of 1.63 (1.51, 1.75) and 0.50 (0.45, 0.55) respectively. The corresponding mean was 5.8 (5.01, 6.69 days). It should be noted that uncertainty increases towards the tail of the distribution: the pooled parameter estimates resulted in a median incubation period of 5.1 (4.5, 5.8) days, whereas the 95th percentile was 11.6 (9.5, 14.2) days.Conclusions and implicationsThe choice of which parameter values are adopted will depend on how the information is used, the associated risks and the perceived consequences of decisions to be taken. These recommendations will need to be revisited once further relevant information becomes available. Finally, we present an RShiny app that facilitates updating these estimates as new data become available.ARTICLE SUMMARYStrengths and limitations of this studyThis study provides a pooled estimate of the distribution of incubation periods which may be used in subsequent modelling studies or to inform decision-makingThis estimate will need to be revisited as subsequent data become available. We present an RShiny app to allow the meta-analysis to be updated with new estimates

Publisher

Cold Spring Harbor Laboratory

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3