ObjectivesThe aim of this study was to conduct a rapid systematic review and meta-analysis of estimates of the incubation period of COVID-19.DesignRapid systematic review and meta-analysis of observational research.SettingInternational studies on incubation period of COVID-19.ParticipantsSearches 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 measuresParameters of a lognormal distribution of incubation periods.ResultsThe 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.ConclusionsThe 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.

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doi: medRxiv preprint Word Count: 3156 2 2 ABSTRACT 2 7Background: Reliable estimates of the incubation period are important for decision making around the 2 8 control of infectious diseases. Knowledge of the incubation period distribution can be used directly to 2 9 inform decision-making or as inputs into mathematical models. 3 0Objectives: The aim of this study was to conduct a rapid systematic review and meta-analysis of 3 1 estimates of the incubation periods of COVID-19. 3 2 Design: Rapid systematic review and meta-analysis of observational research 3 3 Data sources: Publications on the electronic databases PubMed, Google Scholar, MedRxiv and BioRxiv 3 4were searched. The search was not limited to peer-reviewed published data, but also included pre-print 3 5 articles. 6Study appraisal and synthesis methods: Studies were selected for meta-analysis if they reported either 3 7 the parameters and confidence intervals of the distributions fit to the data, or sufficient information to 3 8 facilitate calculation of those values. The majority of studies suitable for inclusion in the final analysis 3 9 modelled incubation period as a lognormal distribution. We conducted a random effects meta-analysis of 4 0 the parameters of this distribution. 4 1 Results: The incubation period distribution may be modelled with a lognormal distribution with pooled 4 2 mu and sigma parameters of 1.63 (1.51, 1.75) and 0.50 (0.45, 0.55) respectively. The corresponding mean 4 3