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Meta-Analysis
. 2021 May 7;100(18):e25837.
doi: 10.1097/MD.0000000000025837.

Assessment of basic reproductive number for COVID-19 at global level: A meta-analysis

Cheng-Jun Yu  1 , Zi-Xiao Wang  2 , Yue Xu  3 , Ming-Xia Hu  1 , Kai Chen  1 , Gang Qin  4
Affiliations
Meta-Analysis

Assessment of basic reproductive number for COVID-19 at global level: A meta-analysis

Cheng-Jun Yu et al. Medicine (Baltimore). .

Abstract

Background: There are large knowledge gaps regarding how transmission of 2019 novel coronavirus disease (COVID-19) occurred in different settings across the world. This study aims to summarize basic reproduction number (R0) data and provide clues for designing prevention and control measures.

Methods: Several databases and preprint platforms were retrieved for literature reporting R0 values of COVID-19. The analysis was stratified by the prespecified modeling method to make the R0 values comparable, and by country/region to explore whether R0 estimates differed across the world. The average R0 values were pooled using a random-effects model.

Results: We identified 185 unique articles, yielding 43 articles for analysis. The selected studies covered 5 countries from Asia, 5 countries from Europe, 12 countries from Africa, and 1 from North America, South America, and Australia each. Exponential growth rate model was most favored by researchers. The pooled global R0 was 4.08 (95% CI, 3.09-5.39). The R0 estimates for new and shifting epicenters were comparable or even higher than that for the original epicenter Wuhan, China.

Conclusions: The high R0 values suggest that an extraordinary combination of control measures is needed for halting COVID-19.

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Conflict of interest statement

The authors have no conflicts of interest to disclose.

Figures

Figure 1
Figure 1
Flow diagram for study selection.
Figure 2
Figure 2
Distribution map of R0 estimates (EGR model-based if not specified) aSEIR model; bGeneralized growth model; cAgent-based model; dMLE model; eSequential Bayesian method; fTime-varying autoregressive state-space model. EGR = exponential growth rate, MLE = maximum likelihood estimation, SEIR = susceptible-exposed-infected-removed.
Figure 3
Figure 3
Forest plot of the pooled EGR model-based R0 estimates. EGR = exponential growth rate.
See this image and copyright information in PMC

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