We estimated the effective reproduction number R(t) for each day of the epidemic using the method of Wallinga & Teunis (2004) . The method assumes that the infectiousness function, which describes the rate at which an infected individual transmits infection over the course of their infection (Becker 1989) , is known. We derived an average infectivity profile from Ferguson et al. (2005) and defined b(a) to be the average relative infectivity of a person on day a of their infection; transmission was assumed not to occur after 10 days. The mean serial interval arising from the resulting infectivity profile was 2.6 days. The method was applied separately to both the death and hospitalization data. Strictly speaking, the method of Wallinga & Teunis (2004) should be applied to incident infections. As infectious events are rarely observed, we (and previous authors) must use symptom onset, death or some other measure as a surrogate marker for infection. Two issues arise, which are as follows. First, notifications of markers (e.g. deaths) may be substantially thinned versions of incident cases. Second, there is a delay, most likely of variable duration, between the infection and the chosen marker. Wallinga & Teunis (2004) showed that a small degree of thinning (e.g. resulting from under-reporting of cases) would not bias estimates of R(t), but did not investigate the effect of using only a small fraction of cases (as when using deaths as a surrogate when the case-fatality rate is low) to estimate R(t). In the Sydney 1919 epidemic, the probability of hospitalization and death for a given clinical infection was 4.8 and 1.2%, respectively. We used repeated stochastic simulations of an epidemic with R 0 in the range of 1.5-2.5 in a population of 800 000, with the number of daily cases thinned to 5 and 1% to confirm that thinning per se results in no discernible bias in the resulting estimates of R over the course of an epidemic. If the delay from the infection to the chosen marker (e.g. death) is fixed, there is no bias in the resulting estimates of R(t). Conversely, if there is variability in the delay, then there is a potential for bias, particularly if the distribution of the delay is right-skewed. The effect of the time-to-marker delay distribution is to widen the epidemic curve of the marker, relative to the true incidence curve. The wider the distribution from the infection time to the marker, the greater the potential bias. On theoretical grounds, it is easy to show that during the early and late exponential phases of an epidemic (i.e. its leading or trailing edge), every marker gives an unbiased estimate of R, provided that the exponential phase is itself long in duration compared with the width of the distribution for the marked event. During the peaks of the epidemic, the epidemic curve is not exponential and the above result does not apply. In our case, Armstrong (1920) provided data on the distribution of time from the onset of influenza symptoms to death (mean 8.86 days, s.d. 6.0 days), which is well described by a gamma (kZ2.74, qZ3.23) distribution. We repeated our epidemic simulations; this time, modelling the time from infection to death using this gamma distribution shifted 1.5 days to the right to account for the disease incubation period (assumed fixed). Applying the method of Wallinga & Teunis (2004) to the death data confirmed that the resulting daily estimate of R has little discernible bias during the early exponential growth period and again during the final days of the epidemic. Our application of the method to death data does underestimate R during the middle of the epidemic, and overestimate it at the start of the declining phase; the extent of the bias increases with increasing R 0 , though it is less than 10% for a freely evolving epidemic with R 0 Z1.5. During periods when R is close to 1, the bias is also small-this is reflected in the similarity of the hospitalization and death results.