Synthetic estimates of COVID-19 mean incubation time

In a recent study posted to the medRxiv* pre-print server, a team of researchers used data from published literature to estimate the mean incubation time of coronavirus disease 2019 (COVID-19), also accommodating heterogeneity of these studies and publication bias.

Study: Estimation of the COVID-19 Average Incubation Time: Systematic Review, Meta-analysis and Sensitivity Analyses. Image Credit: zstock/Shutterstock

The time elapsed from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection to the onset of clinical symptoms is the COVID-19 incubation time.

It varies from patient to patient and helps estimate the average incubation time of the population, a critical metric in developing strategies for isolation or quarantine.

Having a reasonable estimate of the average incubation time could also help in developing an effective intervention strategy when needed. In addition, it could help in the development of epidemic models such as susceptible-exposed-infectives-removed (SEIR).

Overall, the average incubation time is an important parameter to model transmission features of SARS-CoV-2, and variations in it may adversely affect the outcomes. To date, several studies have been done to estimate the average incubation time for COVID-19.

However, available studies do not reveal comparable estimates of the mean incubation time, and their results show considerable variations. It is, thus, difficult to assess the results of which study more reasonably reflects the average incubation time of the population because they evaluate different subjects under different conditions.

About the study

In the present work, the authors provided synthetic estimates of the average incubation time of COVID-19 by using the reports of estimates in the published literature and explored different ways to accommodate heterogeneity involved within these studies. A thorough search covering a long study period from January 1, 2020, to May 20, 2021, helped researchers include more diverse information on estimates of the average incubation times of COVID-19.

They employed Cochran’s heterogeneity statistic Q and Higgin’s & Thompson’s I2 statistic. Subgroup analyses were conducted using mixed-effects models; while they assessed the publication bias using the funnel plot and Egger’s test.

While the authors examined several studies, they filtered 104 studies manually by first checking the abstract and then the full text for the study. They obtained the information of the sample size, as well as the information on either the mean incubation time together with its standard error (SE), or the median incubation time together with a 95% confidence interval (CI), and interquartile range (IQR), or a range.

Four different analyses were conducted during the study, wherein Analysis 1 was conducted for those studies which only estimated the mean incubation time, and Analysis 2 included studies providing information about estimates of the median incubation time. While Analysis 3 combined the studies from both Analyses 1 and 2 and used a transformation to convert the estimates of the median incubation time to those of the mean incubation time, Analysis 4 was conducted for those studies which reported meta-analysis results.

Further, subgroup analyses evaluated estimates with certain controlled heterogeneities. The heterogeneities were for different regions including China, outside China, Hubei, and outside Hubei. In addition, they were for different methodologies, such as parametric models, non-parametric models, and descriptive analysis, and different bias levels, such as low risk, moderate risk, and high risk.

Subgroup analyses, along with sensitivity analyses, further investigated heterogeneity among the reported studies and stabilized the produced synthetic estimates of the mean incubation time of COVID-19.

Study findings

The synthetic estimate of the mean incubation time of COVID-19 from a meta-analysis of 55 estimates was 6.43 days. The meta-analysis yielded the mean incubation time estimate of 6.08 days, which decreased when those 55 estimates were combined with 36 other estimates transformed from the reported estimates of the median incubation time of COVID-19.

Notably, none of the subgroup-analyses differences were significant. For patients outside China and outside Hubei province, the estimate of the mean incubation time was 7.18 days and 6.71 days, respectively. The sub-group analyses for different risk levels showed that studies with low risk of bias yielded the smallest pooled mean estimate of 5.70 days, compared to moderate and high-risk groups. Interestingly, the non-parametric models yielded the highest pooled average mean estimate of 8.30 days.

Sensitivity analyses suggested that including or excluding studies with highly twisted CIs considerably changed the study estimates. 

Limitations and conclusions 

It is difficult to determine the exact average incubation time of COVID-19. The present study still provided great insights into this understated, unknown quantity by incorporating various features of the available estimates, including heterogeneity, varying sample sizes, study bias, differences in estimation methods, and many more.

Although the researchers extensively examined the data of the reported estimates in the literature from several perspectives to produce these estimations, the reported estimates of the mean incubation time of COVID-19 were obtained mainly from the studies covering infected cases before March 31, 2020.  

The limitations of the analyses hindered taking into account the effect of emerging SARS-CoV-2 variants on the average incubation time. Also, the distribution of incubation times was assumed to be right-skewed in some analyses invalidating the normality assumption of the meta-analysis of the study. Further, many studies did not give individual characteristics, such as age, the sex ratio, and medical conditions of patients, which made it difficult to further explore the heterogeneity of the studies.

*Important notice

medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Written by

Neha Mathur

Neha Mathur has a Master’s degree in Biotechnology and extensive experience in digital marketing. She is passionate about reading and music. When she is not working, Neha likes to cook and travel.