Estimation of the COVID-19 Average Incubation Time: Systematic Review, Meta-analysis and Sensitivity Analyses
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Abstract
Objectives
We aim to provide sensible estimates of the average incubation time of COVID-19 by capitalizing available estimates reported in the literature and explore different ways to accommodate heterogeneity involved with the reported studies.
Methods
We search through online databases to collect the studies about estimates of the average incubation time and conduct meta-analyses to accommodate heterogeneity of the studies and the publication bias. Cochran’s heterogeneity statistic Q and Higgin’s & Thompson’s I 2 statistic are employed. Subgroup analyses are conducted using mixed effects models and publication bias is assessed using the funnel plot and Egger’s test.
Results
Using all those reported mean incubation estimates, the average incubation time is estimated to be 6.43 days with a 95% confidence interval (CI) (5.90, 6.96), and using all those reported mean incubation estimates together with those transformed median incubation estimates, the estimated average incubation time is 6.07 days with a 95% CI (5.70,6.45).
Conclusions
Providing sensible estimates of the average incubation time for COVID-19 is important yet complex, and the available results vary considerably due to many factors including heterogeneity and publication bias. We take different angles to estimate the mean incubation time, and our analyses provide estimates to range from 5.68 days to 8.30 days.
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SciScore for 10.1101/2022.01.17.22269421: (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 One summary table reports the proportion of studies with high/low risk of bias for each checklist question, and the other table, called RevMan risk of bias table (Higgins and Thomas, 2011), displays a more detailed view by showing the risk of bias results associated with each study for each question, where the rows correspond to the risk assessment items and the columns refer to the studies; in the display, color red or blue is used to show high and low risk of bias, respectively. 3.6 Publication Bias: To understand the results produced by the meta-analysis, we assess potential … SciScore for 10.1101/2022.01.17.22269421: (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 One summary table reports the proportion of studies with high/low risk of bias for each checklist question, and the other table, called RevMan risk of bias table (Higgins and Thomas, 2011), displays a more detailed view by showing the risk of bias results associated with each study for each question, where the rows correspond to the risk assessment items and the columns refer to the studies; in the display, color red or blue is used to show high and low risk of bias, respectively. 3.6 Publication Bias: To understand the results produced by the meta-analysis, we assess potential publication bias incurred in individual studies. RevMansuggested: (RevMan, RRID:SCR_003581)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:This being said, there are limitations in the analyses here, just like other available studies. Although our search of the literature spans the period of between January 1, 2020, and May 20, 2021, the reported estimates of the mean incubation time of COVID-19 are mainly obtained from the studies of those infected cases prior to March 31, 2020. The results thereby do not reflect the feature that the average incubation time may change with the emerging variants of the virus. The normality assumption in the meta-analysis may not be valid since the distribution of incubation times is assumed to be right-skewed in some analyses. Many studies did not give individual characteristics such as age, the sex ratio, and medical conditions of patients, which hinders us from further exploring the heterogeneity of the studies. Most studies using parametric models assumed a distribution such as Gamma, Weibull, or log-normal to describe COVID-19 incubation times. Such distributional assumptions, however, are basically not testable.
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.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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