the delayed model is,β0 = 1.0, β1 = 0.04β0 , τ = 4, τ2 = 12, I0 = 0.01, t0 = 2/2, t1 = 3/12, t2 = 3/23. For Austria, the parameter for the delayed model is β0 = 0.7, β1 = 0.07β0 , τ = 4, τ2 = 14, I0 = 6, t0 = 3/1, t1 = 3/7, t2 = 3/15. Considering the fact that the value of the activity became 20% of that due to lockdown in Australia, and 30% in Austria, the decline in β was much more drastic compared to the decline in the value of the activity. From the fact that the ratio of β1 to β0 is close to the square of ratio of activity index, we can guess that the value of β might just be proportional to square of activity index6. From this plot, we could immediately see that SEIR model fail to reproduce the effect of time lag between the change in activity and the change in confirmed cases(If we assume time dependence of β is the same as the time dependence of activity). All the above discussions imply, SEIR model FAIL to reproduce the time delay for the effect of lockdown to be realized. Figure 10: value of β in Aus- tralia Figure 11: ˙ Q for actual and SEIR model in Australia Figure 12: ˙ Q for actual and delayed model in Australia Figure 13: value of β in Aus- tria Figure 14: ˙ Q for actual and SEIR model in Austria Figure 15: ˙ Q for actual and delayed model in Austria Let’s try to fit the delayed model for the rest of the countries which expe- rienced successful lockdown. For Czech Republic, we choose the parameter as β0 = 0.6, β1 = 0.08β0 , τ = 4, τ2 = 18, I0 = 6, t0 = 3/6, t1 = 3/7, t2 = 3/14, for Norway we choose the parameter as β0 = 0.2, β1 = 0.19β0 , τ = 4, τ2 = 20, I0 = 100, t0 = 3/1, t1 = 3/5, t2 = 3/12, and for Switzerland we choose as β0 = 0.6, β1 = 0.12β0 , τ = 4, τ2 = 13, I0 = 10, t0 = 2/29, t1 = 3/8, t2 = 3/19. The change of β and the resulting plot for confirmed cases per day will be as in Fig.16 ∼ Fig.21. The ratios of activity index before and after the lockdown are respectively roughly 30% for Czech, 40 ∼ 50% for Norway, and 40% for Switzerland, so square of those numbers roughly correspond to the ratio of β before and after lockdown, again for those countries. 6That actually makes sense, because if we assume for example, only 20% of people go out and the rest stay home, both S and I will be multiplied by 20% and effectively β becomes square of 20%. 6