Table A1.

Basic reproduction numbers from distinct studies (adapted from (Liu et al., 2020))

Study	Location Study	Study date Methods	Methods Approaches	Approaches	R0 estimates (average)	95% CI
Joseph et al.1	Wuhan	31 December 2019–28 January 2020	Stochastic Markov Chain Monte Carlo methods (MCMC)	MCMC methods with Gibbs sampling and non-informative flat prior, using posterior distribution	2.68	2.47–2.86
Shen et al.2	Hubei province	12–22 January 2020	Mathematical model, dynamic compartmental model with population divided into five compartments: susceptible individuals, asymptomatic individuals during the incubation period, infectious individuals with symptoms, isolated individuals with treatment and recovered individuals	R0 = β/α β = mean person-to-person transmission rate/day in the absence of control interventions, using nonlinear least squares method to get its point estimate α = isolation rate = 6	6.49	6.31–6.66
Liu et al.	China and overseas	January 23, 2020 Statistical	Statistical exponential Growth, using SARS generation time = 8.4 days, SD = 3.8 days	Applies Poisson regression to fit the exponential growth rate R0 = 1/M(−r) M = moment generating function of the generation time distribution r = fitted exponential growth rate	2.90	2.32–3.63
Liu et al.	China and overseas	January 23, 2020 Statistical	Statistical maximum likelihood estimation, using SARS generation time = 8.4 days, SD = 3.8 days	Maximize log-likelihood to estimate R0 by using surveillance data during a disease epidemic, and assuming the secondary case is Poisson distribution with expected value R0	2.92	2.28–3.67
Read et al.	China	1–22 January 2020	Mathematical transmission model assuming latent period = 4 days and near to the incubation period	Assumes daily time increments with Poisson-distribution and apply a deterministic SEIR metapopulation transmission model, transmission rate = 1.94, infectious period = 1.61 days	3.11	2.39–4.13
Majumder et al.	Wuhan	December 8, 2019 and January 26, 2020	Mathematical Incidence Decay and Exponential Adjustment (IDEA) model	Adopted mean serial interval lengths from SARS and MERS ranging from 6 to 10 days to fit the IDEA model,	2.55	2.0–3.1
WHO	China	January 18, 2020			1.95	1.4–2.5
Cao et al.	China	January 23, 2020	Mathematical model including compartments Susceptible-Exposed-Infectious- Recovered-Death-Cumulative (SEIRDC)	R = K 2 (L × D) + K (L + D)+1 L = average latent period = 7, D = average latent infectious period = 9, K = logarithmic growth rate of the case count	4.08
Zao et al.	China	10–24 January 2020	Statistical exponential growth model method adopting serial interval from SARS (mean = 8.4 days, SD = 3.8 days) and MERS (mean = 7.6 days, SD = 3.4 days)	Corresponding to 8-fold increase in the reporting rate R0 = 1/M(−r) r = intrinsic growth rate M = moment generating function	2.24	1.96–2.55
Zhao et al.	China	10–24 January 2020	Statistical exponential growth model method adopting serial interval from SARS (mean = 8.4 days, SD = 3.8 days) and MERS (mean = 7.6 days, SD = 3.4 days)	Corresponding to 2-fold increase in the reporting rate R0 = 1/M(−r) r = intrinsic growth rate M = moment generating function	3.58	2.89–4.39
Imai (2020)	Wuhan	January 18, 2020	Mathematical model, computational modelling of potential epidemic trajectories	Assume SARS-like levels of case-to-case variability in the numbers of secondary cases and a SARS-like generation time with 8.4 days, and set number of cases caused by zoonotic exposure and assumed total number of cases to estimate R0 values for best-case, median and worst-case	2.5	1.5–3.5
Julien and Althaus	China and overseas	January 18, 2020	Stochastic simulations of early outbreak trajectories Tang	Stochastic simulations of early outbreak trajectories were performed that are consistent with the epidemiological findings to date	2.2
Tang et al.	China	January 22, 2020	Mathematical SEIR-type epidemiological model incorporates appropriate compartments corresponding to interventions	Method-based method and Likelihood-based method	6.47	5.71–7.23
Qun Li et al.11	China	January 22, 2020	Statistical exponential growth model	Mean incubation period = 5.2 days, mean serial interval = 7.5 days	2.2	1.4–3.9
Steven et al.	China (CDC)			Realistic distributions for the latent and infectious period to calculate R0	5.7	3.8–8.9

Average R₀ = 3.4 Median R₀ = 2.9.