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

Abstract

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.

SciScore for 10.1101/2020.04.24.20073957: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

NIH rigor criteria are not applicable to paper type.

Table 2: Resources

Software and Algorithms
Sentences	Resources
Publications on the electronic databases PubMed, Google Scholar, MedRxiv and BioRxiv were searched with the following keywords: “Novel coronavirus” OR “SARS□CoV□2” OR “2019-nCoV” OR “COVID-19” AND “incubation period” OR “incubation”.	Google Scholar suggested: (Google Scholar, RRID:SCR_008878) BioRxiv suggested: (bioRxiv, RRID:SCR_003933)
The dynamic curated PubMed database “LitCovid” was also monitored, in addition to national and international government reports.	PubMed suggested: (PubMed, RRID:SCR_004846)

Results from OddPub: We did not detect open data. We also did not detect open code. …

SciScore for 10.1101/2020.04.24.20073957: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

NIH rigor criteria are not applicable to paper type.

Table 2: Resources

Software and Algorithms
Sentences	Resources
Publications on the electronic databases PubMed, Google Scholar, MedRxiv and BioRxiv were searched with the following keywords: “Novel coronavirus” OR “SARS□CoV□2” OR “2019-nCoV” OR “COVID-19” AND “incubation period” OR “incubation”.	Google Scholar suggested: (Google Scholar, RRID:SCR_008878) BioRxiv suggested: (bioRxiv, RRID:SCR_003933)
The dynamic curated PubMed database “LitCovid” was also monitored, in addition to national and international government reports.	PubMed suggested: (PubMed, RRID:SCR_004846)

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

One of the weaknesses of our approach is that we extracted and analysed the parameters of the lognormal distribution independently. However, in reality the parameters and the initial distribution that they are fitted to are linked. We were unable to include two studies that did not fit lognormal distributions to the data. However, Figure 6 demonstrates that the impact of removing these studies is likely to be small since they are similar to the pooled estimate, with one falling to the left of the pooled estimate, and the other falling to the right. Ideally, we would have fit distributions to the raw data available from each of the studies, in a way that facilitated the distributions to vary across studies. Such an approach was taken by Lessler et al.[3] in reviewing acute respiratory viral infections. However, the raw data were not available in all cases for the studies that we examined. Another limitation is that many of the papers included in this study used publicly available data to estimate incubation period. Therefore, there is a reasonable chance that several of the analyses have re-used at least some of the same data. In these cases, the studies would not be independent of each other. It is worth noting that the parameter values from our meta-analysis are somewhat higher than previously used in modelling studies. For example, Ferguson et al.[32] used a mean of 5.1 days for incubation period, citing two previous studies.[24, 31] Mean incubation period from our meta ana...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
Thank you for including a protocol registration statement.

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