Method 1. We make use of the likelihood-based method for the simultaneous estimation of initial R t and the SI described by [25] . This method is well suited for estimation of initial R t and SI in real-time with observed aggregated daily counts of new cases, denoted by N = (N 0 , N 1 …,N T ) where T is the last day of observation and N 0 are the initial number of seed cases that begin the outbreak. The N i are assumed to be composed of a mixture of cases that were generated by the previous k days, where k is the maximal value of the serial interval. We denote these as X j , the number of cases that appear on day i that were infected by individuals with onset of symptoms on day j. We assume that the number of infectees generated by infectors with symptoms on day j follows a Poisson distribution with parameter R t N j . Additionally, X j = (X j,j+1 , X j,j+2 …,X j,j+k+1 ), the vector of cases infected by the N j individuals, follows a multinomial distribution with parameters p, k and X j . Here p is a vector of probabilities that denotes the serial interval distribution. Using these assumptions, the following likelihood is obtained: