Table 1.
Descriptive characteristics of the studies included in the meta-analysis
| Study | Country | Model | No. of reproduction | LCL | UCL |
|---|---|---|---|---|---|
| Wu et al., 2020 [14] | China | MCMC | 2.68 | 2.47 | 2.86 |
| Shen et al., 2020 [15] | China | Dynamic compartmental model | 6.49 | 6.31 | 6.66 |
| Liu et al., 2020 [16] | China | Statistical exponential growth model | 2.90 | 2.32 | 3.63 |
| Liu et al., 2020 [16] | China | Statistical maximum likelihood estimation | 2.92 | 2.28 | 3.67 |
| Read et al., 2020 [17] | China | Mathematical transmission model | 3.11 | 2.39 | 4.13 |
| Majumder et al., 2020 [18] | China | IDEA | 2.55 | 2.00 | 3.10 |
| Liu et al., 2020 [11] | China | Mathematical model | 1.95 | 1.40 | 2.50 |
| Zhao et al., 2020 [19] | China | Statistical exponential growth model | 2.24 | 1.96 | 2.55 |
| Zhao et al., 2020 [19] | China | Statistical exponential growth model | 3.58 | 2.89 | 4.39 |
| Imai et al., 2020 [20] | China | Mathematical model | 2.50 | 1.50 | 3.50 |
| Riou et al., 2020 [21] | China | Stochastic simulations of early outbreak trajectories | 2.20 | 1.40 | 3.80 |
| Tang et al., 2020 [22] | China | Mathematical SEIR-type epidemiological model | 6.47 | 5.71 | 7.23 |
| Li et al., 2020 [23] | China | Statistical exponential growth model | 2.20 | 1.40 | 3.90 |
| Zhang et al., 2020 [24] | China | Statistical maximum likelihood estimation | 2.28 | 2.06 | 2.52 |
| Shen et al., 2020 [15] | China | Mathematical model | 4.71 | 4.50 | 4.92 |
| Du et al., 2020 [25] | China | Statistical exponential growth model | 1.90 | 1.47 | 2.59 |
| Muniz-Rodriguez et al., 2020 [26] | China | Statistical exponential growth model | 3.30 | 3.10 | 4.20 |
| Zhou, 2020 [27] | China | SEIR model | 2.12 | 2.04 | 2.18 |
| Liu et al., 2020 [28] | China | Statistical exponential growth model | 4.50 | 4.40 | 4.60 |
| Liu et al., 2020 [28] | China | Statistical exponential growth model | 4.40 | 4.30 | 4.60 |
| Li et al., 2020 [29] | China | Networked dynamic metapopulation model | 2.23 | 1.77 | 3.00 |
| Park et al., 2020 [30] | China | MCMC | 3.10 | 2.10 | 5.70 |
| Shao et al., 2020 [31] | China | Fudan-CCDC model | 3.32 | 3.25 | 3.40 |
| Zhang et al., 2020 [32] | China | SEIQ model | 5.50 | 5.30 | 5.80 |
| Lai et al., 2020 [33] | China | Coalescent-based exponential growth and a birth-death skyline method | 2.60 | 2.10 | 5.10 |
| Jung et al., 2020 [9] | China | MCMC | 2.10 | 2.00 | 2.20 |
| Jung et al., 2020 [9] | China | MCMC | 3.20 | 2.70 | 3.70 |
| Sanche et al., 2020 [34] | China | Statistical exponential growth model | 6.30 | 3.30 | 11.30 |
| Sanche et al., 2020 [34] | China | Statistical exponential growth model | 4.70 | 2.80 | 7.60 |
LCL, lower control limit; UCL, upper control limit; MCMC, Markov chain Monte Carlo; IDEA, incidence decay and exponential adjustment; SEIR, susceptible, exposed, infected, and resistant; CCDC, Chinese Center for Disease Control and Prevention; SEIQ, susceptible, exposed, infected and quarantined.