When all of the data are considered, the results are similar. Without adjustment for missingness R 0 is estimated to be 1.42 and the mean of the serial interval is 1.96 days with a variance of 1.14. Under the constant missingness scheme the reproductive number estimates increase with the attack rate (between 1.49 and 1.54), as well as the mean of the serial interval (2.15-2.25 days). When the reporting fraction increases through time, the reproductive number is smaller and decreases as the AR increases (1.29 to 1.05). The mean of the serial interval follows a similar pattern ranging between 2.01 for AR = 39% and 1.45 for AR = 68%. The results from the overreporting scenarios follow a similar pattern, assuming that the amount of overreporting is consistent throughout the epidemic. The results indicate that if reporting were in fact increasing throughout the epidemic, then the original results could be overstating the magnitude of both the reproductive number and the mean of the serial interval. Table 4 provides the results using the method of Wallinga and Teunis [3] and the exponential growth model to estimate R 0 . The estimates using the MLE estimator of the serial interval (i.e. with mean of 1.96 for WT or 2.09 for exponential) are comparable to those obtained with the MLE method and are 1.48 for the WT method and 1.40 for the exponential method compared to 1.41 for the MLE method. Those with the Fraser et al [16] estimate (mean SI = 1.91 days) tend to differ as a direct function of the mean SI (WT R 0 = 1.57, exponential R 0 = 1.36). Overall the impact of Key: The first block of results consider the entire outbreak and fit the reproductive number using a four parameter logistic function. The second block consider only the first 16 days where the epidemic is in exponential growth. For each attack rate shown (39%, 58% and 68%) estimates are shown when data missingness is assumed to be constant and when it is assumed to follow a logistic pattern with the initial reporting fraction at 0.4 increasing to 0.9.