Supplementary materials: Estimation methods
Method 1: Imported cases were plugged into the reproduction number estimation equation from Wallinga and Teunis (1) , and Cori et al. (2) . As the total number of reported cases (I(t)) at time t (t = 0, 1, 2, 3…) is the sum of local cases (hL(t)) and imported cases (hI(t)), the total number of infections from all the infected cases at time t can be computed as
given ws is a serial interval distribution. Suppose the time-varying reproduction number Rt is the average number of secondary cases generated by each infected individual provided that the conditions remained as they were at time t (2) , the expected number of local infections is
With a pre-determined distribution of ws and assuming the number of local infections follows a Poisson distribution, a likelihood function can be formed (3),
where nL(t) is the observed numbers of local cases on day t and τ is a window width for smoothing an estimate which is usually fixed at 7 days (2).
Method 2: Method 2 is based on the framework of simple susceptible-exposedinfectious-recovered (SEIR) model which classifies a population into one of four compartments at each time point t: susceptible (S(t)), latent (E(t)), infectious (I(t)), or recovered (4, 5) . In the model, a susceptible individual could be infected by either an import case or a local case. We assume hI(t) be the number of imported cases causing
Click here to access/download;Supplementary Material;Supplementary materials.pdf 2 an influx to the disease transmission system and βS(t)I(t)/N be the number of local infected cases (in a latent status) given a local population size N and a transmission rate β. Similar to previous model setting (6, 7) , the model can be described with the following differential equations, which define the rates of subject movement at each time step:
where 1/α is the average length of latent period and 1/γ is the length of infectious period. With αE(t) being the expected number of local cases reported and Poissondistributed outcomes assumed, a likelihood function from the transmission model is
By solving the likelihood, we can obtain the Rt as βS(t)/Nγ (4, 5) .
In method 1, we assumed the serial interval follows a lognormal distribution with mean 7.5 days and standard deviation 3.4 days (8) . In method 2, we assumed the mean latent duration was fixed at 5.2 days (8) and the mean infectious duration was 2.3 days (i.e. difference between mean serial interval and latent duration). Maximum likelihood methods were used to solve the model parameters based on the time series data of the confirmed cases. For method 2, we also tested a longer (4 days) and a shorter (1.5 days) infectious period given the mean serial interval unchanged (so latent periods are respectively equal to 3.5 days and 6 days). 3 Figure S1 . Estimated instantaneous reproduction number (Rt) (solid line) and 95% confidence intervals (dotted lines) using when (A) mean infectious period = 4 days and latent period = 3.5 days, and (B) mean infectious period = 1.5 day and latent period = 6 days.