Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Australia of about 0.765% each day. That corresponds to a doubling of the numbers approx. every 91 days.
The graph above and the following table show the course of reported coronavirus infections in Australia assuming that the numbers are following an exponential trend without any slowdown.
6,440

6,460
+22 (+0.342%)
6,520
+60 (+0.929%)
6,550
+25 (+0.383%)
6,550
+0 (+0%)
6,550
+0 (+0%)
6,550
+0 (+0%)
6,550
+0 (+0%)
6,660
+114 (+1.74%)
6,680
+16 (+0.24%)
6,730
6,690  6,780
+58 (+0.867%)
6,790
6,740  6,840
+52 (+0.765%)
6,840
6,790  6,890
+52 (+0.765%)
6,890
6,840  6,940
+52 (+0.765%)
6,940
6,890  6,990
+53 (+0.765%)
7,000
6,950  7,050
+53 (+0.765%)
7,050
7,000  7,100
+54 (+0.765%)
7,100
7,050  7,160
+54 (+0.765%)
7,160
7,110  7,210
+54 (+0.765%)
7,210
7,160  7,270
+55 (+0.765%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Australia of about 6.26% each day. That corresponds to a doubling of the numbers approx. every 11 days.
The graph above and the following table show the course of reported deaths by coronavirus in Australia assuming that the numbers are following an exponential trend without any slowdown.
63

63
+0 (+0%)
66
+3 (+4.76%)
67
+1 (+1.52%)
67
+0 (+0%)
67
+0 (+0%)
67
+0 (+0%)
67
+0 (+0%)
75
+8 (+11.9%)
79
+4 (+5.33%)
84
80  88
+5 (+5.8%)
89
85  93
+5 (+6.26%)
94
90  99
+6 (+6.26%)
100
96  105
+6 (+6.26%)
107
102  112
+6 (+6.26%)
113
108  119
+7 (+6.26%)
120
115  126
+7 (+6.26%)
128
122  134
+8 (+6.26%)
136
130  142
+8 (+6.26%)
144
138  151
+9 (+6.26%)
In Australia, approx. 0.707% of the population die each year. With a population of roughly 25,000,000 people in Australia, that corresponds to about 484 deaths per day on the statistical average.
The graph above shows the reported daily deaths by coronavirus in contrast to the statistical number as baseline.
To estimate the mortality rate of coronavirus infections, we need to consider that a reported death already showed up in the reported cases a few days before. It is presumed that this lag is between 7 and 14 days. If we assumed that the lag was about 7 days, the mortality rate in Australia would be approx. 1.2%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Australia in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Australia in the previous days.
The graph tries to predict the number of required intensive care units in Australia. We assume the following:
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Australian Capital Territory of about 0.676% each day. That corresponds to a doubling of the numbers approx. every 100 days.
The graph above and the following table show the course of reported coronavirus infections in Australian Capital Territory assuming that the numbers are following an exponential trend without any slowdown.
103

103
+0 (+0%)
103
+0 (+0%)
103
+0 (+0%)
103
+0 (+0%)
103
+0 (+0%)
103
+0 (+0%)
103
+0 (+0%)
104
+1 (+0.971%)
105
+1 (+0.962%)
106
105  106
+0 (+0.484%)
106
106  107
+0 (+0.676%)
107
106  108
+0 (+0.676%)
108
107  108
+0 (+0.676%)
108
108  109
+0 (+0.676%)
109
109  110
+0 (+0.676%)
110
109  110
+0 (+0.676%)
111
110  111
+0 (+0.676%)
111
111  112
+0 (+0.676%)
112
112  113
+0 (+0.676%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Australian Capital Territory of about 2.91e10% each day. That corresponds to a doubling of the numbers approx. every ,240,000,000,000 days.
The graph above and the following table show the course of reported deaths by coronavirus in Australian Capital Territory assuming that the numbers are following an exponential trend without any slowdown.
3

3
+0 (+0%)
3
+0 (+0%)
3
+0 (+0%)
3
+0 (+0%)
3
+0 (+0%)
3
+0 (+0%)
3
+0 (+0%)
3
+0 (+0%)
3
+0 (+0%)
3
3  3
+0 (+7.28e10%)
3
3  3
+0 (+2.91e10%)
3
3  3
+0 (+2.91e10%)
3
3  3
+0 (+2.91e10%)
3
3  3
+0 (+2.91e10%)
3
3  3
+0 (+2.91e10%)
3
3  3
+0 (+2.91e10%)
3
3  3
+0 (+2.91e10%)
3
3  3
+0 (+2.91e10%)
3
3  3
+0 (+2.91e10%)
To estimate the mortality rate of coronavirus infections, we need to consider that a reported death already showed up in the reported cases a few days before. It is presumed that this lag is between 7 and 14 days. If we assumed that the lag was about 7 days, the mortality rate in Australian Capital Territory would be approx. 2.9%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Australian Capital Territory in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Australian Capital Territory in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in New South Wales of about 0.741% each day. That corresponds to a doubling of the numbers approx. every 94 days.
The graph above and the following table show the course of reported coronavirus infections in New South Wales assuming that the numbers are following an exponential trend without any slowdown.
2,890

2,900
+11 (+0.381%)
2,930
+29 (+1%)
2,930
+0 (+0%)
2,930
+0 (+0%)
2,930
+0 (+0%)
2,930
+0 (+0%)
2,930
+0 (+0%)
2,980
+50 (+1.71%)
2,980
+6 (+0.202%)
3,010
2,990  3,030
+25 (+0.851%)
3,030
3,010  3,050
+22 (+0.741%)
3,050
3,030  3,070
+22 (+0.741%)
3,070
3,050  3,100
+23 (+0.741%)
3,100
3,080  3,120
+23 (+0.741%)
3,120
3,100  3,140
+23 (+0.741%)
3,140
3,120  3,170
+23 (+0.741%)
3,170
3,140  3,190
+23 (+0.741%)
3,190
3,170  3,210
+23 (+0.741%)
3,210
3,190  3,240
+24 (+0.741%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in New South Wales of about 9.32% each day. That corresponds to a doubling of the numbers approx. every 7.8 days.
The graph above and the following table show the course of reported deaths by coronavirus in New South Wales assuming that the numbers are following an exponential trend without any slowdown.
25

25
+0 (+0%)
26
+1 (+4%)
26
+0 (+0%)
26
+0 (+0%)
26
+0 (+0%)
26
+0 (+0%)
26
+0 (+0%)
31
+5 (+19.2%)
33
+2 (+6.45%)
36
34  39
+3 (+9.19%)
39
37  42
+3 (+9.32%)
43
40  46
+4 (+9.32%)
47
44  51
+4 (+9.32%)
51
48  55
+4 (+9.32%)
56
52  60
+5 (+9.32%)
62
57  66
+5 (+9.32%)
67
63  72
+6 (+9.32%)
74
68  79
+6 (+9.32%)
80
75  86
+7 (+9.32%)
To estimate the mortality rate of coronavirus infections, we need to consider that a reported death already showed up in the reported cases a few days before. It is presumed that this lag is between 7 and 14 days. If we assumed that the lag was about 7 days, the mortality rate in New South Wales would be approx. 1.1%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in New South Wales in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in New South Wales in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Northern Territory of about 0.363% each day. That corresponds to a doubling of the numbers approx. every ,190 days.
The graph above and the following table show the course of reported coronavirus infections in Northern Territory assuming that the numbers are following an exponential trend without any slowdown.
28

28
+0 (+0%)
28
+0 (+0%)
28
+0 (+0%)
28
+0 (+0%)
28
+0 (+0%)
28
+0 (+0%)
28
+0 (+0%)
27
+1 (+3.57%)
28
+1 (+3.7%)
27
27  28
+0 (+1.8%)
27
27  28
+0 (+0.363%)
27
26  28
+0 (+0.363%)
27
26  28
+0 (+0.363%)
27
26  28
+0 (+0.363%)
27
26  28
+0 (+0.363%)
27
26  28
+0 (+0.363%)
27
26  28
+0 (+0.363%)
27
26  28
+0 (+0.363%)
27
26  27
+0 (+0.363%)
The graph above and the following table show the course of reported deaths by coronavirus in Northern Territory assuming that the numbers are following an exponential trend without any slowdown.
0

0
+0 (+0%)
0
+0 (+0%)
0
+0 (+0%)
0
+0 (+0%)
0
+0 (+0%)
0
+0 (+0%)
0
+0 (+0%)
0
+0 (+0%)
0
+0 (+0%)
To estimate the mortality rate of coronavirus infections, we need to consider that a reported death already showed up in the reported cases a few days before. It is presumed that this lag is between 7 and 14 days. If we assumed that the lag was about 7 days, the mortality rate in Northern Territory would be approx. 0%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Northern Territory in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Northern Territory in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Queensland of about 0.432% each day. That corresponds to a doubling of the numbers approx. every 160 days.
The graph above and the following table show the course of reported coronavirus infections in Queensland assuming that the numbers are following an exponential trend without any slowdown.
999

1,000
+2 (+0.2%)
1,010
+6 (+0.599%)
1,020
+8 (+0.794%)
1,020
+0 (+0%)
1,020
+0 (+0%)
1,020
+0 (+0%)
1,020
+0 (+0%)
1,030
+11 (+1.08%)
1,030
+0 (+0%)
1,030
1,030  1,040
+6 (+0.54%)
1,040
1,030  1,040
+4 (+0.432%)
1,040
1,040  1,050
+4 (+0.432%)
1,040
1,040  1,050
+4 (+0.432%)
1,050
1,040  1,050
+5 (+0.432%)
1,050
1,050  1,060
+5 (+0.432%)
1,060
1,050  1,060
+5 (+0.432%)
1,060
1,060  1,070
+5 (+0.432%)
1,070
1,060  1,070
+5 (+0.432%)
1,070
1,070  1,080
+5 (+0.432%)
The graph above and the following table show the course of reported deaths by coronavirus in Queensland assuming that the numbers are following an exponential trend without any slowdown.
5

5
+0 (+0%)
5
+0 (+0%)
6
+1 (+20%)
6
+0 (+0%)
6
+0 (+0%)
6
+0 (+0%)
6
+0 (+0%)
6
+0 (+0%)
6
+0 (+0%)
6
6  6
+0 (+0%)
6
6  6
+0 (+0%)
6
6  6
+0 (+0%)
6
6  6
+0 (+0%)
6
6  6
+0 (+0%)
6
6  6
+0 (+0%)
6
6  6
+0 (+0%)
6
6  6
+0 (+0%)
6
6  6
+0 (+0%)
6
6  6
+0 (+0%)
To estimate the mortality rate of coronavirus infections, we need to consider that a reported death already showed up in the reported cases a few days before. It is presumed that this lag is between 7 and 14 days. If we assumed that the lag was about 7 days, the mortality rate in Queensland would be approx. 0.6%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Queensland in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Queensland in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in South Australia of about 0.275% each day. That corresponds to a doubling of the numbers approx. every 250 days.
The graph above and the following table show the course of reported coronavirus infections in South Australia assuming that the numbers are following an exponential trend without any slowdown.
433

433
+0 (+0%)
435
+2 (+0.462%)
435
+0 (+0%)
435
+0 (+0%)
435
+0 (+0%)
435
+0 (+0%)
435
+0 (+0%)
438
+3 (+0.69%)
438
+0 (+0%)
440
438  441
+2 (+0.344%)
441
439  442
+1 (+0.275%)
442
441  443
+1 (+0.275%)
443
442  445
+1 (+0.275%)
444
443  446
+1 (+0.275%)
446
444  447
+1 (+0.275%)
447
445  448
+1 (+0.275%)
448
447  449
+1 (+0.275%)
449
448  451
+1 (+0.275%)
451
449  452
+1 (+0.275%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in South Australia of about 2.91e10% each day. That corresponds to a doubling of the numbers approx. every ,240,000,000,000 days.
The graph above and the following table show the course of reported deaths by coronavirus in South Australia assuming that the numbers are following an exponential trend without any slowdown.
4

4
+0 (+0%)
4
+0 (+0%)
4
+0 (+0%)
4
+0 (+0%)
4
+0 (+0%)
4
+0 (+0%)
4
+0 (+0%)
4
+0 (+0%)
4
+0 (+0%)
4
4  4
+0 (+7.28e10%)
4
4  4
+0 (+2.91e10%)
4
4  4
+0 (+2.91e10%)
4
4  4
+0 (+2.91e10%)
4
4  4
+0 (+2.91e10%)
4
4  4
+0 (+2.91e10%)
4
4  4
+0 (+2.91e10%)
4
4  4
+0 (+2.91e10%)
4
4  4
+0 (+2.91e10%)
4
4  4
+0 (+2.91e10%)
To estimate the mortality rate of coronavirus infections, we need to consider that a reported death already showed up in the reported cases a few days before. It is presumed that this lag is between 7 and 14 days. If we assumed that the lag was about 7 days, the mortality rate in South Australia would be approx. 0.92%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in South Australia in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in South Australia in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Tasmania of about 5.75% each day. That corresponds to a doubling of the numbers approx. every 12 days.
The graph above and the following table show the course of reported coronavirus infections in Tasmania assuming that the numbers are following an exponential trend without any slowdown.
165

169
+4 (+2.42%)
180
+11 (+6.51%)
180
+0 (+0%)
180
+0 (+0%)
180
+0 (+0%)
180
+0 (+0%)
180
+0 (+0%)
207
+27 (+15%)
207
+0 (+0%)
222
209  236
+15 (+7.24%)
235
221  250
+13 (+5.75%)
248
233  264
+13 (+5.75%)
263
247  279
+14 (+5.75%)
278
261  296
+15 (+5.75%)
294
276  313
+16 (+5.75%)
310
292  330
+17 (+5.75%)
328
308  349
+18 (+5.75%)
347
326  370
+19 (+5.75%)
367
345  391
+20 (+5.75%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Tasmania of about 9.28% each day. That corresponds to a doubling of the numbers approx. every 7.8 days.
The graph above and the following table show the course of reported deaths by coronavirus in Tasmania assuming that the numbers are following an exponential trend without any slowdown.
6

6
+0 (+0%)
7
+1 (+16.7%)
7
+0 (+0%)
7
+0 (+0%)
7
+0 (+0%)
7
+0 (+0%)
7
+0 (+0%)
8
+1 (+14.3%)
9
+1 (+12.5%)
10
9  10
+0 (+6.9%)
11
10  11
+0 (+9.28%)
11
11  12
+0 (+9.28%)
13
12  13
+1 (+9.28%)
14
13  15
+1 (+9.28%)
15
14  16
+1 (+9.28%)
16
15  18
+1 (+9.28%)
18
17  19
+2 (+9.28%)
20
18  21
+2 (+9.28%)
21
20  23
+2 (+9.28%)
To estimate the mortality rate of coronavirus infections, we need to consider that a reported death already showed up in the reported cases a few days before. It is presumed that this lag is between 7 and 14 days. If we assumed that the lag was about 7 days, the mortality rate in Tasmania would be approx. 5%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Tasmania in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Tasmania in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Victoria of about 0.679% each day. That corresponds to a doubling of the numbers approx. every 100 days.
The graph above and the following table show the course of reported coronavirus infections in Victoria assuming that the numbers are following an exponential trend without any slowdown.
1,300

1,300
+0 (+0%)
1,300
+3 (+0.231%)
1,320
+17 (+1.31%)
1,320
+0 (+0%)
1,320
+0 (+0%)
1,320
+0 (+0%)
1,320
+0 (+0%)
1,340
+18 (+1.36%)
1,340
+6 (+0.449%)
1,350
1,340  1,360
+9 (+0.68%)
1,360
1,350  1,370
+9 (+0.679%)
1,370
1,360  1,380
+9 (+0.679%)
1,380
1,370  1,390
+9 (+0.679%)
1,390
1,380  1,400
+9 (+0.679%)
1,400
1,390  1,410
+9 (+0.679%)
1,410
1,400  1,420
+9 (+0.679%)
1,420
1,410  1,430
+10 (+0.679%)
1,430
1,420  1,440
+10 (+0.679%)
1,440
1,430  1,440
+10 (+0.679%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Victoria of about 5.49% each day. That corresponds to a doubling of the numbers approx. every 13 days.
The graph above and the following table show the course of reported deaths by coronavirus in Victoria assuming that the numbers are following an exponential trend without any slowdown.
14

14
+0 (+0%)
14
+0 (+0%)
14
+0 (+0%)
14
+0 (+0%)
14
+0 (+0%)
14
+0 (+0%)
14
+0 (+0%)
16
+2 (+14.3%)
16
+0 (+0%)
17
16  18
+1 (+6.9%)
18
17  19
+0 (+5.49%)
19
18  20
+0 (+5.49%)
20
19  21
+1 (+5.49%)
21
20  22
+1 (+5.49%)
22
21  24
+1 (+5.49%)
24
22  25
+1 (+5.49%)
25
23  26
+1 (+5.49%)
26
25  28
+1 (+5.49%)
28
26  29
+1 (+5.49%)
To estimate the mortality rate of coronavirus infections, we need to consider that a reported death already showed up in the reported cases a few days before. It is presumed that this lag is between 7 and 14 days. If we assumed that the lag was about 7 days, the mortality rate in Victoria would be approx. 1.2%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Victoria in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Victoria in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Western Australia of about 0.479% each day. That corresponds to a doubling of the numbers approx. every 150 days.
The graph above and the following table show the course of reported coronavirus infections in Western Australia assuming that the numbers are following an exponential trend without any slowdown.
527

532
+5 (+0.949%)
541
+9 (+1.69%)
541
+0 (+0%)
541
+0 (+0%)
541
+0 (+0%)
541
+0 (+0%)
541
+0 (+0%)
546
+5 (+0.924%)
548
+2 (+0.366%)
551
548  553
+3 (+0.461%)
553
551  555
+3 (+0.479%)
556
554  558
+3 (+0.479%)
558
556  561
+3 (+0.479%)
561
559  563
+3 (+0.479%)
564
562  566
+3 (+0.479%)
567
564  569
+3 (+0.479%)
569
567  571
+3 (+0.479%)
572
570  574
+3 (+0.479%)
575
573  577
+3 (+0.479%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Western Australia of about 4.09% each day. That corresponds to a doubling of the numbers approx. every 17 days.
The graph above and the following table show the course of reported deaths by coronavirus in Western Australia assuming that the numbers are following an exponential trend without any slowdown.
6

6
+0 (+0%)
7
+1 (+16.7%)
7
+0 (+0%)
7
+0 (+0%)
7
+0 (+0%)
7
+0 (+0%)
7
+0 (+0%)
7
+0 (+0%)
8
+1 (+14.3%)
8
7  9
+0 (+3.81e10%)
8
8  9
+0 (+4.09%)
9
8  9
+0 (+4.09%)
9
8  10
+0 (+4.09%)
9
9  10
+0 (+4.09%)
10
9  11
+0 (+4.09%)
10
9  11
+0 (+4.09%)
11
10  11
+0 (+4.09%)
11
10  12
+0 (+4.09%)
11
11  12
+0 (+4.09%)
To estimate the mortality rate of coronavirus infections, we need to consider that a reported death already showed up in the reported cases a few days before. It is presumed that this lag is between 7 and 14 days. If we assumed that the lag was about 7 days, the mortality rate in Western Australia would be approx. 1.5%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Western Australia in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Western Australia in the previous days.
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