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

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Title: Incubation period of COVID-19: a rapid systematic review and meta-analysis of observational research
Authors: McAloon, Conor G.Collins, Áine B.Hunt, KevinBarber, AnnByrne, Andrew W.Butler, FrancisCasey, MiriamGriffin, John M.Lane, ElizabethMcEvoy, DavidWall, Patrick G.Green, M. (Martin)O'Grady, LukeMore, Simon John
Permanent link: http://hdl.handle.net/10197/11488
Date: 16-Aug-2020
Online since: 2020-08-24T12:25:44Z
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-19CoronavirusIncubation periodsModelling studiesSystematic reviews
DOI: 10.1136/bmjopen-2020-039652
Language: en
Status of Item: Peer reviewed
Appears in Collections:Biosystems and Food Engineering Research Collection
Public Health, Physiotherapy and Sports Science Research Collection
Veterinary Medicine Research Collection
CVERA Research Collection

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