Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Germany of about 1.5% each day. That corresponds to a doubling of the numbers approx. every 47 days.
The graph above and the following table show the course of reported coronavirus infections in Germany assuming that the numbers are following an exponential trend without any slowdown.
135,000

138,000
+2,950 (+2.19%)
141,000
+3,700 (+2.69%)
143,000
+1,950 (+1.38%)
145,000
+1,840 (+1.29%)
147,000
+1,880 (+1.3%)
148,000
+1,230 (+0.834%)
151,000
+2,360 (+1.59%)
153,000
+2,480 (+1.65%)
155,000
+1,870 (+1.22%)
158,000
157,000  158,000
+2,510 (+1.62%)
160,000
160,000  160,000
+2,370 (+1.5%)
162,000
162,000  163,000
+2,400 (+1.5%)
165,000
164,000  165,000
+2,440 (+1.5%)
167,000
167,000  168,000
+2,470 (+1.5%)
170,000
169,000  170,000
+2,510 (+1.5%)
172,000
172,000  173,000
+2,550 (+1.5%)
175,000
174,000  175,000
+2,590 (+1.5%)
177,000
177,000  178,000
+2,630 (+1.5%)
180,000
180,000  180,000
+2,660 (+1.5%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Germany of about 4.7% each day. That corresponds to a doubling of the numbers approx. every 15 days.
The graph above and the following table show the course of reported deaths by coronavirus in Germany assuming that the numbers are following an exponential trend without any slowdown.
3,800

4,050
+248 (+6.52%)
4,350
+300 (+7.4%)
4,460
+107 (+2.46%)
4,590
+127 (+2.85%)
4,860
+276 (+6.02%)
5,030
+171 (+3.52%)
5,280
+246 (+4.89%)
5,580
+296 (+5.61%)
5,760
+185 (+3.32%)
6,060
6,000  6,120
+302 (+5.25%)
6,350
6,280  6,410
+285 (+4.7%)
6,650
6,580  6,710
+298 (+4.7%)
6,960
6,890  7,030
+312 (+4.7%)
7,280
7,210  7,360
+327 (+4.7%)
7,630
7,550  7,700
+342 (+4.7%)
7,990
7,910  8,070
+359 (+4.7%)
8,360
8,280  8,440
+375 (+4.7%)
8,750
8,670  8,840
+393 (+4.7%)
9,170
9,080  9,260
+411 (+4.7%)
In Germany, approx. 1.17% of the population die each year. With a population of roughly 82,900,000 people in Germany, that corresponds to about 2,660 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 Germany would be approx. 4.1%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Germany in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Germany in the previous days.
The graph tries to predict the number of required intensive care units in Germany. We assume the following:
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in BadenWürttemberg of about 1.23% each day. That corresponds to a doubling of the numbers approx. every 57 days.
The graph above and the following table show the course of reported coronavirus infections in BadenWürttemberg assuming that the numbers are following an exponential trend without any slowdown.
26,500

27,300
+715 (+2.69%)
27,900
+625 (+2.29%)
28,300
+370 (+1.33%)
28,700
+459 (+1.62%)
28,900
+186 (+0.648%)
29,400
+545 (+1.89%)
29,400
+0 (+0%)
29,800
+349 (+1.19%)
30,200
+377 (+1.27%)
30,400
30,100  30,800
+274 (+0.908%)
30,800
30,500  31,100
+373 (+1.23%)
31,200
30,900  31,500
+378 (+1.23%)
31,600
31,200  31,900
+382 (+1.23%)
32,000
31,600  32,300
+387 (+1.23%)
32,400
32,000  32,700
+392 (+1.23%)
32,800
32,400  33,100
+396 (+1.23%)
33,200
32,800  33,500
+401 (+1.23%)
33,600
33,200  33,900
+406 (+1.23%)
34,000
33,600  34,300
+411 (+1.23%)
The graph above shows the daily increase of reported coronavirus infections in BadenWürttemberg in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Bayern of about 1.45% each day. That corresponds to a doubling of the numbers approx. every 48 days.
The graph above and the following table show the course of reported coronavirus infections in Bayern assuming that the numbers are following an exponential trend without any slowdown.
36,000

36,900
+854 (+2.37%)
37,400
+526 (+1.43%)
37,800
+442 (+1.18%)
38,300
+461 (+1.22%)
38,800
+504 (+1.32%)
39,400
+581 (+1.5%)
39,400
+0 (+0%)
39,900
+544 (+1.38%)
40,500
+608 (+1.52%)
41,000
40,500  41,500
+433 (+1.07%)
41,600
41,100  42,100
+595 (+1.45%)
42,200
41,700  42,700
+603 (+1.45%)
42,800
42,300  43,300
+612 (+1.45%)
43,400
42,900  43,900
+621 (+1.45%)
44,000
43,500  44,600
+630 (+1.45%)
44,700
44,100  45,200
+639 (+1.45%)
45,300
44,800  45,900
+649 (+1.45%)
46,000
45,400  46,600
+658 (+1.45%)
46,700
46,100  47,200
+668 (+1.45%)
The graph above shows the daily increase of reported coronavirus infections in Bayern in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Berlin of about 1.87% each day. That corresponds to a doubling of the numbers approx. every 37 days.
The graph above and the following table show the course of reported coronavirus infections in Berlin assuming that the numbers are following an exponential trend without any slowdown.
4,950

5,070
+121 (+2.45%)
5,160
+93 (+1.84%)
5,200
+37 (+0.717%)
5,240
+41 (+0.789%)
5,310
+75 (+1.43%)
5,320
+12 (+0.226%)
5,320
+0 (+0%)
5,460
+135 (+2.54%)
5,530
+66 (+1.21%)
5,610
5,510  5,720
+86 (+1.56%)
5,720
5,610  5,820
+105 (+1.87%)
5,820
5,720  5,930
+107 (+1.87%)
5,930
5,820  6,040
+109 (+1.87%)
6,040
5,930  6,160
+111 (+1.87%)
6,160
6,040  6,270
+113 (+1.87%)
6,270
6,160  6,390
+115 (+1.87%)
6,390
6,270  6,510
+117 (+1.87%)
6,510
6,390  6,630
+119 (+1.87%)
6,630
6,510  6,750
+122 (+1.87%)
The graph above shows the daily increase of reported coronavirus infections in Berlin in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Brandenburg of about 3.61% each day. That corresponds to a doubling of the numbers approx. every 20 days.
The graph above and the following table show the course of reported coronavirus infections in Brandenburg assuming that the numbers are following an exponential trend without any slowdown.
2,120

2,160
+41 (+1.93%)
2,220
+60 (+2.78%)
2,240
+17 (+0.765%)
2,280
+37 (+1.65%)
2,390
+114 (+5.01%)
2,450
+58 (+2.43%)
2,450
+0 (+0%)
2,540
+89 (+3.64%)
2,630
+91 (+3.59%)
2,700
2,620  2,780
+71 (+2.7%)
2,800
2,710  2,880
+97 (+3.61%)
2,900
2,810  2,990
+101 (+3.61%)
3,000
2,910  3,100
+105 (+3.61%)
3,110
3,020  3,210
+108 (+3.61%)
3,220
3,120  3,320
+112 (+3.61%)
3,340
3,240  3,440
+116 (+3.61%)
3,460
3,350  3,570
+121 (+3.61%)
3,580
3,480  3,700
+125 (+3.61%)
3,710
3,600  3,830
+129 (+3.61%)
The graph above shows the daily increase of reported coronavirus infections in Brandenburg in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Bremen of about 3.44% each day. That corresponds to a doubling of the numbers approx. every 21 days.
The graph above and the following table show the course of reported coronavirus infections in Bremen assuming that the numbers are following an exponential trend without any slowdown.
556

567
+11 (+1.98%)
585
+18 (+3.17%)
604
+19 (+3.25%)
609
+5 (+0.828%)
624
+15 (+2.46%)
672
+48 (+7.69%)
672
+0 (+0%)
707
+35 (+5.21%)
719
+12 (+1.7%)
741
714  768
+22 (+3%)
766
738  795
+25 (+3.44%)
792
764  822
+26 (+3.44%)
820
790  850
+27 (+3.44%)
848
817  880
+28 (+3.44%)
877
845  910
+29 (+3.44%)
907
874  941
+30 (+3.44%)
938
904  974
+31 (+3.44%)
971
935  1,010
+32 (+3.44%)
1,000
968  1,040
+33 (+3.44%)
The graph above shows the daily increase of reported coronavirus infections in Bremen in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Hamburg of about 1.32% each day. That corresponds to a doubling of the numbers approx. every 53 days.
The graph above and the following table show the course of reported coronavirus infections in Hamburg assuming that the numbers are following an exponential trend without any slowdown.
4,010

4,120
+113 (+2.82%)
4,170
+49 (+1.19%)
4,190
+18 (+0.432%)
4,200
+19 (+0.454%)
4,200
+0 (+0%)
4,290
+82 (+1.95%)
4,290
+0 (+0%)
4,360
+72 (+1.68%)
4,400
+42 (+0.964%)
4,450
4,390  4,500
+47 (+1.08%)
4,510
4,450  4,560
+59 (+1.32%)
4,570
4,510  4,620
+60 (+1.32%)
4,630
4,570  4,690
+60 (+1.32%)
4,690
4,630  4,750
+61 (+1.32%)
4,750
4,690  4,810
+62 (+1.32%)
4,810
4,750  4,870
+63 (+1.32%)
4,880
4,810  4,940
+64 (+1.32%)
4,940
4,880  5,000
+64 (+1.32%)
5,010
4,940  5,070
+65 (+1.32%)
The graph above shows the daily increase of reported coronavirus infections in Hamburg in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Hessen of about 1.59% each day. That corresponds to a doubling of the numbers approx. every 44 days.
The graph above and the following table show the course of reported coronavirus infections in Hessen assuming that the numbers are following an exponential trend without any slowdown.
6,710

6,920
+211 (+3.15%)
7,110
+192 (+2.78%)
7,180
+69 (+0.971%)
7,230
+54 (+0.752%)
7,380
+149 (+2.06%)
7,590
+213 (+2.89%)
7,590
+0 (+0%)
7,710
+119 (+1.57%)
7,840
+125 (+1.62%)
7,930
7,820  8,040
+93 (+1.19%)
8,060
7,950  8,170
+126 (+1.59%)
8,180
8,070  8,300
+128 (+1.59%)
8,320
8,200  8,430
+130 (+1.59%)
8,450
8,330  8,560
+133 (+1.59%)
8,580
8,470  8,700
+135 (+1.59%)
8,720
8,600  8,840
+137 (+1.59%)
8,860
8,740  8,980
+139 (+1.59%)
9,000
8,880  9,120
+141 (+1.59%)
9,140
9,020  9,270
+143 (+1.59%)
The graph above shows the daily increase of reported coronavirus infections in Hessen in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in MecklenburgVorpommern of about 0.605% each day. That corresponds to a doubling of the numbers approx. every 110 days.
The graph above and the following table show the course of reported coronavirus infections in MecklenburgVorpommern assuming that the numbers are following an exponential trend without any slowdown.
634

645
+11 (+1.74%)
651
+6 (+0.93%)
653
+2 (+0.307%)
655
+2 (+0.306%)
656
+1 (+0.153%)
659
+3 (+0.457%)
659
+0 (+0%)
661
+2 (+0.303%)
667
+6 (+0.908%)
670
666  673
+3 (+0.378%)
674
670  677
+4 (+0.605%)
678
674  681
+4 (+0.605%)
682
678  685
+4 (+0.605%)
686
682  689
+4 (+0.605%)
690
687  693
+4 (+0.605%)
694
691  698
+4 (+0.605%)
698
695  702
+4 (+0.605%)
703
699  706
+4 (+0.605%)
707
703  710
+4 (+0.605%)
The graph above shows the daily increase of reported coronavirus infections in MecklenburgVorpommern in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Niedersachsen of about 1.58% each day. That corresponds to a doubling of the numbers approx. every 44 days.
The graph above and the following table show the course of reported coronavirus infections in Niedersachsen assuming that the numbers are following an exponential trend without any slowdown.
8,440

8,650
+207 (+2.45%)
8,800
+148 (+1.71%)
8,900
+103 (+1.17%)
9,100
+198 (+2.22%)
9,240
+138 (+1.52%)
9,390
+155 (+1.68%)
9,390
+0 (+0%)
9,540
+153 (+1.63%)
9,690
+147 (+1.54%)
9,810
9,670  9,940
+116 (+1.2%)
9,960
9,830  10,100
+155 (+1.58%)
10,100
9,980  10,300
+158 (+1.58%)
10,300
10,100  10,400
+160 (+1.58%)
10,400
10,300  10,600
+163 (+1.58%)
10,600
10,500  10,800
+166 (+1.58%)
10,800
10,600  10,900
+168 (+1.58%)
10,900
10,800  11,100
+171 (+1.58%)
11,100
11,000  11,300
+173 (+1.58%)
11,300
11,100  11,500
+176 (+1.58%)
The graph above shows the daily increase of reported coronavirus infections in Niedersachsen in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in NordrheinWestfalen of about 1.43% each day. That corresponds to a doubling of the numbers approx. every 49 days.
The graph above and the following table show the course of reported coronavirus infections in NordrheinWestfalen assuming that the numbers are following an exponential trend without any slowdown.
27,000

28,000
+976 (+3.61%)
28,500
+465 (+1.66%)
29,000
+500 (+1.76%)
29,400
+418 (+1.44%)
30,200
+796 (+2.71%)
30,600
+399 (+1.32%)
30,600
+0 (+0%)
31,100
+522 (+1.71%)
31,500
+359 (+1.15%)
31,800
31,400  32,200
+359 (+1.14%)
32,300
31,900  32,700
+455 (+1.43%)
32,700
32,300  33,200
+462 (+1.43%)
33,200
32,800  33,700
+468 (+1.43%)
33,700
33,200  34,100
+475 (+1.43%)
34,200
33,700  34,600
+482 (+1.43%)
34,700
34,200  35,100
+489 (+1.43%)
35,100
34,700  35,600
+496 (+1.43%)
35,700
35,200  36,100
+503 (+1.43%)
36,200
35,700  36,600
+510 (+1.43%)
The graph above shows the daily increase of reported coronavirus infections in NordrheinWestfalen in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Repatriierte of about 3.64e10% each day. That corresponds to a doubling of the numbers approx. every 190,000,000,000 days.
The graph above and the following table show the course of reported coronavirus infections in Repatriierte assuming that the numbers are following an exponential trend without any slowdown.
2

2
+0 (+0%)
2
+0 (+0%)
2
+0 (+0%)
2
+0 (+0%)
2
+0 (+0%)
2
2  2
+0 (+7.28e10%)
2
2  2
+0 (+3.64e10%)
2
2  2
+0 (+3.64e10%)
2
2  2
+0 (+3.64e10%)
2
2  2
+0 (+3.64e10%)
2
2  2
+0 (+3.64e10%)
2
2  2
+0 (+3.64e10%)
2
2  2
+0 (+3.64e10%)
2
2  2
+0 (+3.64e10%)
2
2  2
+0 (+3.64e10%)
The graph above shows the daily increase of reported coronavirus infections in Repatriierte in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in RheinlandPfalz of about 1.09% each day. That corresponds to a doubling of the numbers approx. every 64 days.
The graph above and the following table show the course of reported coronavirus infections in RheinlandPfalz assuming that the numbers are following an exponential trend without any slowdown.
5,210

5,320
+113 (+2.17%)
5,430
+108 (+2.03%)
5,520
+91 (+1.68%)
5,560
+38 (+0.688%)
5,590
+32 (+0.575%)
5,640
+50 (+0.894%)
5,640
+0 (+0%)
5,730
+88 (+1.56%)
5,770
+36 (+0.628%)
5,820
5,760  5,890
+54 (+0.935%)
5,880
5,820  5,950
+64 (+1.09%)
5,950
5,880  6,020
+64 (+1.09%)
6,010
5,950  6,080
+65 (+1.09%)
6,080
6,010  6,150
+66 (+1.09%)
6,150
6,080  6,220
+66 (+1.09%)
6,210
6,140  6,280
+67 (+1.09%)
6,280
6,210  6,350
+68 (+1.09%)
6,350
6,280  6,420
+69 (+1.09%)
6,420
6,350  6,490
+69 (+1.09%)
The graph above shows the daily increase of reported coronavirus infections in RheinlandPfalz in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Saarland of about 1.51% each day. That corresponds to a doubling of the numbers approx. every 46 days.
The graph above and the following table show the course of reported coronavirus infections in Saarland assuming that the numbers are following an exponential trend without any slowdown.
2,250

2,290
+35 (+1.55%)
2,300
+14 (+0.612%)
2,310
+11 (+0.478%)
2,330
+14 (+0.605%)
2,370
+39 (+1.68%)
2,400
+28 (+1.18%)
2,400
+0 (+0%)
2,450
+50 (+2.09%)
2,470
+23 (+0.941%)
2,500
2,460  2,540
+31 (+1.28%)
2,540
2,500  2,580
+38 (+1.51%)
2,580
2,540  2,620
+38 (+1.51%)
2,610
2,570  2,660
+39 (+1.51%)
2,650
2,610  2,700
+40 (+1.51%)
2,690
2,650  2,740
+40 (+1.51%)
2,740
2,690  2,780
+41 (+1.51%)
2,780
2,730  2,820
+41 (+1.51%)
2,820
2,780  2,860
+42 (+1.51%)
2,860
2,820  2,910
+43 (+1.51%)
The graph above shows the daily increase of reported coronavirus infections in Saarland in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Sachsen of about 0.909% each day. That corresponds to a doubling of the numbers approx. every 77 days.
The graph above and the following table show the course of reported coronavirus infections in Sachsen assuming that the numbers are following an exponential trend without any slowdown.
4,050

4,140
+92 (+2.27%)
4,190
+50 (+1.21%)
4,230
+39 (+0.931%)
4,250
+24 (+0.568%)
4,270
+20 (+0.47%)
4,330
+54 (+1.26%)
4,330
+0 (+0%)
4,380
+50 (+1.16%)
4,410
+29 (+0.663%)
4,440
4,400  4,480
+33 (+0.742%)
4,480
4,440  4,520
+40 (+0.909%)
4,520
4,480  4,560
+41 (+0.909%)
4,560
4,520  4,600
+41 (+0.909%)
4,600
4,560  4,640
+41 (+0.909%)
4,640
4,600  4,680
+42 (+0.909%)
4,690
4,650  4,730
+42 (+0.909%)
4,730
4,690  4,770
+43 (+0.909%)
4,770
4,730  4,810
+43 (+0.909%)
4,820
4,770  4,860
+43 (+0.909%)
The graph above shows the daily increase of reported coronavirus infections in Sachsen in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in SachsenAnhalt of about 1.91% each day. That corresponds to a doubling of the numbers approx. every 37 days.
The graph above and the following table show the course of reported coronavirus infections in SachsenAnhalt assuming that the numbers are following an exponential trend without any slowdown.
1,280

1,320
+36 (+2.81%)
1,350
+35 (+2.66%)
1,370
+19 (+1.41%)
1,380
+14 (+1.02%)
1,400
+12 (+0.868%)
1,430
+30 (+2.15%)
1,430
+0 (+0%)
1,440
+11 (+0.772%)
1,480
+44 (+3.06%)
1,500
1,470  1,520
+17 (+1.15%)
1,530
1,500  1,550
+29 (+1.91%)
1,550
1,530  1,580
+29 (+1.91%)
1,580
1,560  1,610
+30 (+1.91%)
1,610
1,590  1,640
+30 (+1.91%)
1,650
1,620  1,670
+31 (+1.91%)
1,680
1,650  1,700
+31 (+1.91%)
1,710
1,680  1,740
+32 (+1.91%)
1,740
1,710  1,770
+33 (+1.91%)
1,780
1,750  1,800
+33 (+1.91%)
The graph above shows the daily increase of reported coronavirus infections in SachsenAnhalt in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in SchleswigHolstein of about 1.61% each day. That corresponds to a doubling of the numbers approx. every 43 days.
The graph above and the following table show the course of reported coronavirus infections in SchleswigHolstein assuming that the numbers are following an exponential trend without any slowdown.
2,350

2,390
+38 (+1.62%)
2,420
+30 (+1.26%)
2,430
+9 (+0.372%)
2,410
+12 (+0.495%)
2,500
+82 (+3.4%)
2,530
+34 (+1.36%)
2,530
+0 (+0%)
2,560
+27 (+1.07%)
2,610
+55 (+2.15%)
2,640
2,610  2,670
+28 (+1.07%)
2,680
2,650  2,720
+42 (+1.61%)
2,730
2,690  2,760
+43 (+1.61%)
2,770
2,730  2,810
+44 (+1.61%)
2,810
2,780  2,850
+45 (+1.61%)
2,860
2,820  2,900
+45 (+1.61%)
2,910
2,870  2,940
+46 (+1.61%)
2,950
2,910  2,990
+47 (+1.61%)
3,000
2,960  3,040
+47 (+1.61%)
3,050
3,010  3,090
+48 (+1.61%)
The graph above shows the daily increase of reported coronavirus infections in SchleswigHolstein in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Thüringen of about 3.21% each day. That corresponds to a doubling of the numbers approx. every 22 days.
The graph above and the following table show the course of reported coronavirus infections in Thüringen assuming that the numbers are following an exponential trend without any slowdown.
1,680

1,720
+35 (+2.08%)
1,760
+39 (+2.27%)
1,790
+29 (+1.65%)
1,800
+13 (+0.728%)
1,870
+74 (+4.12%)
1,930
+60 (+3.21%)
1,930
+0 (+0%)
2,020
+91 (+4.71%)
2,060
+35 (+1.73%)
2,110
2,040  2,190
+57 (+2.77%)
2,180
2,110  2,260
+68 (+3.21%)
2,250
2,180  2,330
+70 (+3.21%)
2,330
2,250  2,400
+72 (+3.21%)
2,400
2,320  2,480
+75 (+3.21%)
2,480
2,390  2,560
+77 (+3.21%)
2,560
2,470  2,640
+79 (+3.21%)
2,640
2,550  2,730
+82 (+3.21%)
2,720
2,630  2,820
+85 (+3.21%)
2,810
2,720  2,910
+87 (+3.21%)
The graph above shows the daily increase of reported coronavirus infections in Thüringen in the previous days.
The German Federal Ministry of Health recommends:
We do not guarantee the accuracy of the information.
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