The distribution of detection times ( Figure 5 ) is to show whether the temporal pattern in the background seasonal ILI cases will have any effect on pandemic detection. The detection specificity (Sp) and the pandemic case reporting rate (α) were set for all methods at the following values: Sp = 99% and α = 5%. Detection times are typically within 5-6 weeks for our method (Figure 5a ). This compares favourably with the ILI rate threshold method at η = 34 cases per 100000 population (Figure 5b ). The slightly longer detection times are present for the starting weeks falling in the period of late-November to mid-January (week 9 to week 15 in Figure  5a ). This is the time period when the seasonal ILI incidences show widespread and peak influenza activities ( Figure 1 ). In the case of simulated pandemics starting during this period, the above two factors together mask the probability of (WCR, N HB ) as seasonal one in the first few weeks of pandemic. This masking causes delay in generating an alarm and the delay is more pronounced at lower case reporting rates and a higher specificity and is, for example, responsible for reducing the method's sensitivity to 98% at α = 0.5% and Sp = 99%. Conversely, during the same time-period the threshold method produces its shortest detection times because the peak-level seasonal cases having been added to the pandemic cases help the weekly ILI rate quickly cross the rate threshold η. However, our method outperforms the ILI rate threshold method in the beginning and end of an influenza season. Mov-Avg Cusum performs poorly in terms of detection times in the first few weeks of the season (Figure 5c ). This is because it requires 9 weeks to calculate the first Mov-Avg Cusum statistic. This situation improves as we move well within the seasonal period. But even for the later starting weeks, the method takes comparatively longer time to detect a pandemic. However, this method also outperforms the ILI rate threshold method towards the end of a season.