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  5. Incubation period of COVID-19: a rapid systematic review and meta-analysis of observational research
 
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Incubation period of COVID-19: a rapid systematic review and meta-analysis of observational research

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Author(s)
McAloon, Conor G. 
Collins, Áine B. 
Hunt, Kevin 
Barber, Ann 
Byrne, Andrew W. 
Butler, Francis 
Casey, Miriam 
Griffin, John M. 
Lane, Elizabeth 
McEvoy, David 
Wall, Patrick G. 
Green, M. (Martin) 
O'Grady, Luke 
More, Simon John 
Uri
http://hdl.handle.net/10197/11488
Date Issued
16 August 2020
Date Available
24T12:25:44Z August 2020
Abstract
Objectives: The aim of this study was to conduct a rapid systematic review and meta-analysis of estimates of the incubation period of COVID-19. Design: Rapid systematic review and meta-analysis of observational research. Setting: International studies on incubation period of COVID-19. Participants: Searches were carried out in PubMed, Google Scholar, Embase, Cochrane Library as well as the preprint servers MedRxiv and BioRxiv. Studies were selected for meta-analysis if they reported either the parameters and CIs of the distributions fit to the data, or sufficient information to facilitate calculation of those values. After initial eligibility screening, 24 studies were selected for initial review, nine of these were shortlisted for meta-analysis. Final estimates are from meta-analysis of eight studies. Primary outcome measures: Parameters of a lognormal distribution of incubation periods. Results: The incubation period distribution may be modelled with a lognormal distribution with pooled mu and sigma parameters (95% CIs) of 1.63 (95% CI 1.51 to 1.75) and 0.50 (95% CI 0.46 to 0.55), respectively. The corresponding mean (95% CIs) was 5.8 (95% CI 5.0 to 6.7) days. It should be noted that uncertainty increases towards the tail of the distribution: the pooled parameter estimates (95% CIs) resulted in a median incubation period of 5.1 (95% CI 4.5 to 5.8) days, whereas the 95th percentile was 11.7 (95% CI 9.7 to 14.2) days. Conclusions: The 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. Accordingly, we present an R Shiny app that facilitates updating these estimates as new data become available.
Type of Material
Journal Article
Publisher
BMJ
Journal
BMJ Open
Volume
10
Issue
8
Copyright (Published Version)
2020 the Authors or their employers
Keywords
  • COVID-19

  • Coronavirus

  • Incubation periods

  • Modelling studies

  • Systematic reviews

DOI
10.1136/bmjopen-2020-039652
Language
English
Status of Item
Peer reviewed
ISSN
2044-6055
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Owning collection
Veterinary Medicine Research Collection
Scopus© citations
235
Acquisition Date
Jan 26, 2023
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Views
705
Acquisition Date
Jan 26, 2023
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Downloads
237
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6
Acquisition Date
Jan 26, 2023
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