Given that we are predominantly interested in the reproduction numbers during the periods of early exponential growth and during the final cases of the epidemic ( §4.5), we consider that the method of Wallinga & Teunis (2004) produces estimates of R that are adequate for our purposes. To remove dayto-day variation in estimates of R(t) for the purpose of making inference, we fitted a smooth curve to the daily estimates of R(t) using cubic splines with knots every 7 days. Figure 2b shows the daily estimates of the effective reproduction number R(t) based on both the hospitalization and death data. The estimates are noisier during the periods when case numbers are small (e.g. before day 75 and after day 200). As expected,RðtÞ begins above 1 and drops below 1 as the first wave peaks, though not by much ðR min ðC ÞZ 0:85G0:01ðGs:e:ÞÞ. It returns to greater than 1 at approximately day 130 (figure 2b), which is approximately when the second wave of the epidemic began to grow, and remained above 1 until day 165, the peak of the second wave. It is apparent that RðtÞ based on the hospital admissions underestimates R(t) during both waves due to hospitals being overloaded (figure 2b). At times other than early in the epidemic when the number of deaths is very small, the estimates of R(t) based on either hospitalizations or deaths are very similar (allowing for deaths to lag hospitalized cases; figure 2b). We henceforth use deaths only to make inference on R(t). Indeed, we expect there to be less bias in the estimates of R(t) arising from deaths compared with hospitalizations. This is because being admitted to hospital is dependent on many factors unrelated to the epidemiology of disease that may vary over time (e.g. perceived need for hospital care based on the case-fatality rate). The maximum value of the smoothed curve during period B gave an estimate ofRðBÞZ 1:59G0:02ðGs:e:Þ (figure 2b). The mean of the daily reproduction number in period F waŝ RðFÞZ 0:95G0:04ðGs:e:Þ.