Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in United States of America of about 3.67% each day. That corresponds to a doubling of the numbers approx. every 19 days.
The graph above and the following table show the course of reported coronavirus infections in United States of America assuming that the numbers are following an exponential trend without any slowdown.
636,000

668,000
+31,500 (+4.94%)
700,000
+31,900 (+4.78%)
732,000
+32,500 (+4.64%)
759,000
+26,900 (+3.67%)
784,000
+25,200 (+3.33%)
812,000
+27,500 (+3.51%)
840,000
+28,400 (+3.49%)
869,000
+29,000 (+3.45%)
905,000
+36,200 (+4.16%)
937,000
933,000  940,000
+31,400 (+3.47%)
971,000
968,000  975,000
+34,400 (+3.67%)
1,010,000
1,000,000  1,010,000
+35,700 (+3.67%)
1,040,000
1,040,000  1,050,000
+37,000 (+3.67%)
1,080,000
1,080,000  1,090,000
+38,300 (+3.67%)
1,120,000
1,120,000  1,130,000
+39,800 (+3.67%)
1,160,000
1,160,000  1,170,000
+41,200 (+3.67%)
1,210,000
1,200,000  1,210,000
+42,700 (+3.67%)
1,250,000
1,250,000  1,250,000
+44,300 (+3.67%)
1,300,000
1,290,000  1,300,000
+45,900 (+3.67%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in United States of America of about 5.52% 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 United States of America assuming that the numbers are following an exponential trend without any slowdown.
28,300

32,900
+4,590 (+16.2%)
36,800
+3,860 (+11.7%)
38,700
+1,890 (+5.14%)
40,700
+2,000 (+5.17%)
42,100
+1,430 (+3.52%)
44,400
+2,350 (+5.58%)
46,600
+2,180 (+4.9%)
50,000
+3,330 (+7.15%)
51,900
+2,000 (+3.99%)
55,100
54,400  55,800
+3,130 (+6.02%)
58,100
57,400  58,800
+3,040 (+5.52%)
61,300
60,600  62,100
+3,210 (+5.52%)
64,700
63,900  65,500
+3,380 (+5.52%)
68,300
67,400  69,100
+3,570 (+5.52%)
72,000
71,200  72,900
+3,770 (+5.52%)
76,000
75,100  77,000
+3,980 (+5.52%)
80,200
79,200  81,200
+4,190 (+5.52%)
84,600
83,600  85,700
+4,430 (+5.52%)
89,300
88,200  90,400
+4,670 (+5.52%)
In United States of America, approx. 0.815% of the population die each year. With a population of roughly 327,000,000 people in United States of America, that corresponds to about 7,310 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 United States of America would be approx. 7.4%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in United States of America in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in United States of America in the previous days.
The graph tries to predict the number of required intensive care units in United States of America. We assume the following:
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Alabama of about 4.26% 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 coronavirus infections in Alabama assuming that the numbers are following an exponential trend without any slowdown.
4,080

4,350
+270 (+6.63%)
4,570
+226 (+5.2%)
4,710
+141 (+3.08%)
4,890
+176 (+3.74%)
5,080
+191 (+3.91%)
5,320
+238 (+4.69%)
5,590
+276 (+5.19%)
5,830
+239 (+4.27%)
6,030
+194 (+3.33%)
6,310
6,250  6,370
+285 (+4.73%)
6,580
6,520  6,640
+269 (+4.26%)
6,860
6,800  6,920
+280 (+4.26%)
7,150
7,090  7,220
+292 (+4.26%)
7,460
7,390  7,520
+305 (+4.26%)
7,780
7,710  7,850
+318 (+4.26%)
8,110
8,030  8,180
+331 (+4.26%)
8,450
8,380  8,530
+346 (+4.26%)
8,810
8,730  8,890
+360 (+4.26%)
9,190
9,110  9,270
+376 (+4.26%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Alabama of about 4.38% each day. That corresponds to a doubling of the numbers approx. every 16 days.
The graph above and the following table show the course of reported deaths by coronavirus in Alabama assuming that the numbers are following an exponential trend without any slowdown.
118

133
+15 (+12.7%)
148
+15 (+11.3%)
153
+5 (+3.38%)
157
+4 (+2.61%)
163
+6 (+3.82%)
183
+20 (+12.3%)
196
+13 (+7.1%)
202
+6 (+3.06%)
209
+7 (+3.47%)
220
215  224
+11 (+5.06%)
229
225  234
+10 (+4.38%)
239
235  244
+10 (+4.38%)
250
245  255
+10 (+4.38%)
261
256  266
+11 (+4.38%)
272
267  277
+11 (+4.38%)
284
278  290
+12 (+4.38%)
296
291  302
+12 (+4.38%)
309
303  316
+13 (+4.38%)
323
317  329
+14 (+4.38%)
In United States of America, approx. 0.815% of the population die each year. With a population of roughly 4,900,000 people in Alabama, that corresponds to about 109 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 Alabama would be approx. 4.6%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Alabama in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Alabama in the previous days.
The graph tries to predict the number of required intensive care units in Alabama. We assume the following:
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Alaska of about 0.962% each day. That corresponds to a doubling of the numbers approx. every 72 days.
The graph above and the following table show the course of reported coronavirus infections in Alaska assuming that the numbers are following an exponential trend without any slowdown.
293

300
+7 (+2.39%)
309
+9 (+3%)
314
+5 (+1.62%)
319
+5 (+1.59%)
321
+2 (+0.627%)
329
+8 (+2.49%)
335
+6 (+1.82%)
337
+2 (+0.597%)
339
+2 (+0.593%)
343
341  345
+4 (+1.21%)
346
344  349
+3 (+0.962%)
350
347  352
+3 (+0.962%)
353
351  355
+3 (+0.962%)
356
354  359
+3 (+0.962%)
360
358  362
+3 (+0.962%)
363
361  366
+3 (+0.962%)
367
364  369
+3 (+0.962%)
370
368  373
+4 (+0.962%)
374
372  376
+4 (+0.962%)
The graph above and the following table show the course of reported deaths by coronavirus in Alaska assuming that the numbers are following an exponential trend without any slowdown.
9

9
+0 (+0%)
9
+0 (+0%)
9
+0 (+0%)
9
+0 (+0%)
9
+0 (+0%)
9
+0 (+0%)
9
+0 (+0%)
9
+0 (+0%)
9
+0 (+0%)
9
9  9
+0 (+2.22e14%)
9
9  9
+0 (+0%)
9
9  9
+0 (+0%)
9
9  9
+0 (+0%)
9
9  9
+0 (+0%)
9
9  9
+0 (+0%)
9
9  9
+0 (+0%)
9
9  9
+0 (+0%)
9
9  9
+0 (+0%)
9
9  9
+0 (+0%)
In United States of America, approx. 0.815% of the population die each year. With a population of roughly 732,000 people in Alaska, that corresponds to about 16 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 Alaska 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 Alaska in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Alaska in the previous days.
The graph tries to predict the number of required intensive care units in Alaska. We assume the following:
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Arizona of about 4.84% 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 coronavirus infections in Arizona assuming that the numbers are following an exponential trend without any slowdown.
3,960

4,240
+273 (+6.89%)
4,510
+274 (+6.47%)
4,720
+213 (+4.72%)
4,930
+209 (+4.42%)
5,070
+135 (+2.74%)
5,260
+188 (+3.71%)
5,470
+217 (+4.13%)
5,770
+299 (+5.46%)
6,050
+273 (+4.73%)
6,330
6,300  6,370
+290 (+4.79%)
6,640
6,610  6,680
+307 (+4.84%)
6,960
6,930  7,000
+322 (+4.84%)
7,300
7,260  7,340
+337 (+4.84%)
7,650
7,610  7,690
+353 (+4.84%)
8,020
7,980  8,070
+371 (+4.84%)
8,410
8,370  8,460
+388 (+4.84%)
8,820
8,770  8,870
+407 (+4.84%)
9,250
9,200  9,300
+427 (+4.84%)
9,690
9,640  9,750
+448 (+4.84%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Arizona of about 8.47% each day. That corresponds to a doubling of the numbers approx. every 8.5 days.
The graph above and the following table show the course of reported deaths by coronavirus in Arizona assuming that the numbers are following an exponential trend without any slowdown.
142

150
+8 (+5.63%)
169
+19 (+12.7%)
180
+11 (+6.51%)
184
+4 (+2.22%)
191
+7 (+3.8%)
208
+17 (+8.9%)
231
+23 (+11.1%)
249
+18 (+7.79%)
266
+17 (+6.83%)
291
285  297
+25 (+9.41%)
316
309  322
+25 (+8.47%)
342
336  349
+27 (+8.47%)
371
364  379
+29 (+8.47%)
403
395  411
+31 (+8.47%)
437
428  446
+34 (+8.47%)
474
465  484
+37 (+8.47%)
514
504  525
+40 (+8.47%)
558
547  569
+44 (+8.47%)
605
593  617
+47 (+8.47%)
In United States of America, approx. 0.815% of the population die each year. With a population of roughly 7,280,000 people in Arizona, that corresponds to about 163 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 Arizona would be approx. 5.9%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Arizona in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Arizona in the previous days.
The graph tries to predict the number of required intensive care units in Arizona. We assume the following:
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Arkansas of about 12.4% each day. That corresponds to a doubling of the numbers approx. every 5.9 days.
The graph above and the following table show the course of reported coronavirus infections in Arkansas assuming that the numbers are following an exponential trend without any slowdown.
1,570

1,620
+51 (+3.25%)
1,700
+75 (+4.63%)
1,740
+49 (+2.89%)
1,780
+37 (+2.12%)
1,970
+192 (+10.8%)
1,990
+17 (+0.862%)
2,280
+286 (+14.4%)
2,600
+323 (+14.2%)
2,810
+211 (+8.12%)
3,210
3,110  3,310
+401 (+14.3%)
3,610
3,500  3,720
+398 (+12.4%)
4,060
3,930  4,180
+447 (+12.4%)
4,560
4,420  4,700
+502 (+12.4%)
5,120
4,970  5,280
+565 (+12.4%)
5,760
5,590  5,940
+635 (+12.4%)
6,470
6,280  6,670
+713 (+12.4%)
7,270
7,050  7,500
+802 (+12.4%)
8,170
7,930  8,430
+901 (+12.4%)
9,190
8,910  9,470
+1,010 (+12.4%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Arkansas of about 4.15% 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 Arkansas assuming that the numbers are following an exponential trend without any slowdown.
33

37
+4 (+12.1%)
37
+0 (+0%)
38
+1 (+2.7%)
39
+1 (+2.63%)
41
+2 (+5.13%)
42
+1 (+2.44%)
42
+0 (+0%)
45
+3 (+7.14%)
47
+2 (+4.44%)
49
47  50
+2 (+3.51%)
51
49  52
+2 (+4.15%)
53
51  54
+2 (+4.15%)
55
53  57
+2 (+4.15%)
57
56  59
+2 (+4.15%)
60
58  61
+2 (+4.15%)
62
60  64
+2 (+4.15%)
65
63  67
+3 (+4.15%)
67
65  69
+3 (+4.15%)
70
68  72
+3 (+4.15%)
In United States of America, approx. 0.815% of the population die each year. With a population of roughly 3,020,000 people in Arkansas, that corresponds to about 67 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 Arkansas would be approx. 2.8%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Arkansas in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Arkansas in the previous days.
The graph tries to predict the number of required intensive care units in Arkansas. We assume the following:
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in California of about 5.32% 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 coronavirus infections in California assuming that the numbers are following an exponential trend without any slowdown.
26,700

27,700
+991 (+3.71%)
29,200
+1,480 (+5.35%)
30,500
+1,330 (+4.58%)
31,400
+940 (+3.08%)
33,700
+2,260 (+7.17%)
35,500
+1,780 (+5.28%)
37,300
+1,880 (+5.3%)
39,600
+2,220 (+5.94%)
41,400
+1,790 (+4.53%)
43,700
43,400  43,900
+2,320 (+5.62%)
46,000
45,700  46,300
+2,320 (+5.32%)
48,500
48,200  48,700
+2,450 (+5.32%)
51,000
50,700  51,300
+2,580 (+5.32%)
53,700
53,400  54,100
+2,720 (+5.32%)
56,600
56,300  56,900
+2,860 (+5.32%)
59,600
59,300  60,000
+3,010 (+5.32%)
62,800
62,400  63,200
+3,170 (+5.32%)
66,100
65,800  66,500
+3,340 (+5.32%)
69,700
69,300  70,100
+3,520 (+5.32%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in California of about 8.11% each day. That corresponds to a doubling of the numbers approx. every 8.9 days.
The graph above and the following table show the course of reported deaths by coronavirus in California assuming that the numbers are following an exponential trend without any slowdown.
860

956
+96 (+11.2%)
1,040
+81 (+8.47%)
1,140
+103 (+9.93%)
1,180
+37 (+3.25%)
1,230
+48 (+4.08%)
1,280
+57 (+4.65%)
1,420
+139 (+10.8%)
1,530
+112 (+7.88%)
1,620
+88 (+5.74%)
1,770
1,730  1,810
+152 (+9.35%)
1,920
1,870  1,960
+144 (+8.11%)
2,070
2,020  2,120
+155 (+8.11%)
2,240
2,190  2,290
+168 (+8.11%)
2,420
2,360  2,480
+182 (+8.11%)
2,620
2,560  2,680
+196 (+8.11%)
2,830
2,760  2,900
+212 (+8.11%)
3,060
2,990  3,130
+229 (+8.11%)
3,310
3,230  3,390
+248 (+8.11%)
3,580
3,490  3,660
+268 (+8.11%)
In United States of America, approx. 0.815% of the population die each year. With a population of roughly 39,500,000 people in California, that corresponds to about 882 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 California would be approx. 5.6%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in California in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in California in the previous days.
The graph tries to predict the number of required intensive care units in California. We assume the following:
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Colorado of about 5.2% each day. That corresponds to a doubling of the numbers approx. every 14 days.
The graph above and the following table show the course of reported coronavirus infections in Colorado assuming that the numbers are following an exponential trend without any slowdown.
7,960

8,290
+330 (+4.15%)
8,690
+405 (+4.89%)
9,050
+356 (+4.1%)
9,730
+683 (+7.55%)
9,730
+0 (+0%)
10,500
+743 (+7.64%)
10,900
+418 (+3.99%)
11,300
+387 (+3.55%)
12,300
+978 (+8.67%)
12,700
12,400  13,000
+462 (+3.77%)
13,400
13,000  13,700
+661 (+5.2%)
14,100
13,700  14,400
+695 (+5.2%)
14,800
14,400  15,200
+731 (+5.2%)
15,600
15,200  16,000
+769 (+5.2%)
16,400
16,000  16,800
+809 (+5.2%)
17,200
16,800  17,700
+851 (+5.2%)
18,100
17,700  18,600
+896 (+5.2%)
19,100
18,600  19,600
+942 (+5.2%)
20,100
19,600  20,600
+991 (+5.2%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Colorado of about 11.5% each day. That corresponds to a doubling of the numbers approx. every 6.4 days.
The graph above and the following table show the course of reported deaths by coronavirus in Colorado assuming that the numbers are following an exponential trend without any slowdown.
328

355
+27 (+8.23%)
372
+17 (+4.79%)
389
+17 (+4.57%)
420
+31 (+7.97%)
420
+0 (+0%)
483
+63 (+15%)
506
+23 (+4.76%)
552
+46 (+9.09%)
674
+122 (+22.1%)
721
666  779
+47 (+6.9%)
803
743  869
+83 (+11.5%)
895
828  968
+92 (+11.5%)
998
923  1,080
+103 (+11.5%)
1,110
1,030  1,200
+115 (+11.5%)
1,240
1,150  1,340
+128 (+11.5%)
1,380
1,280  1,500
+142 (+11.5%)
1,540
1,430  1,670
+159 (+11.5%)
1,720
1,590  1,860
+177 (+11.5%)
1,920
1,770  2,070
+197 (+11.5%)
In United States of America, approx. 0.815% of the population die each year. With a population of roughly 5,760,000 people in Colorado, that corresponds to about 129 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 Colorado would be approx. 7.8%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Colorado in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Colorado in the previous days.
The graph tries to predict the number of required intensive care units in Colorado. We assume the following:
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Connecticut of about 5.27% each day. That corresponds to a doubling of the numbers approx. every 14 days.
The graph above and the following table show the course of reported coronavirus infections in Connecticut assuming that the numbers are following an exponential trend without any slowdown.
14,800

15,900
+1,130 (+7.65%)
16,800
+925 (+5.82%)
17,600
+741 (+4.41%)
18,000
+412 (+2.35%)
19,800
+1,850 (+10.3%)
20,400
+545 (+2.75%)
22,500
+2,110 (+10.4%)
23,100
+631 (+2.81%)
23,900
+836 (+3.62%)
25,500
24,600  26,400
+1,560 (+6.52%)
26,800
25,900  27,800
+1,340 (+5.27%)
28,300
27,200  29,300
+1,410 (+5.27%)
29,700
28,700  30,800
+1,490 (+5.27%)
31,300
30,200  32,500
+1,570 (+5.27%)
33,000
31,800  34,200
+1,650 (+5.27%)
34,700
33,500  36,000
+1,740 (+5.27%)
36,500
35,200  37,900
+1,830 (+5.27%)
38,400
37,100  39,800
+1,920 (+5.27%)
40,500
39,000  41,900
+2,020 (+5.27%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Connecticut of about 7.35% each day. That corresponds to a doubling of the numbers approx. every 9.8 days.
The graph above and the following table show the course of reported deaths by coronavirus in Connecticut assuming that the numbers are following an exponential trend without any slowdown.
868

971
+103 (+11.9%)
1,040
+65 (+6.69%)
1,090
+50 (+4.83%)
1,130
+41 (+3.78%)
1,330
+204 (+18.1%)
1,420
+92 (+6.91%)
1,540
+121 (+8.5%)
1,640
+95 (+6.15%)
1,770
+128 (+7.81%)
1,900
1,880  1,910
+129 (+7.32%)
2,040
2,020  2,050
+139 (+7.35%)
2,190
2,170  2,210
+150 (+7.35%)
2,350
2,330  2,370
+161 (+7.35%)
2,520
2,500  2,540
+172 (+7.35%)
2,700
2,680  2,730
+185 (+7.35%)
2,900
2,880  2,930
+199 (+7.35%)
3,120
3,090  3,140
+213 (+7.35%)
3,340
3,310  3,370
+229 (+7.35%)
3,590
3,560  3,620
+246 (+7.35%)
In United States of America, approx. 0.815% of the population die each year. With a population of roughly 3,570,000 people in Connecticut, that corresponds to about 80 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 Connecticut would be approx. 11%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Connecticut in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Connecticut in the previous days.
The graph tries to predict the number of required intensive care units in Connecticut. We assume the following:
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Delaware of about 5.29% 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 coronavirus infections in Delaware assuming that the numbers are following an exponential trend without any slowdown.
2,010

2,070
+56 (+2.78%)
2,320
+247 (+11.9%)
2,540
+221 (+9.54%)
2,540
+0 (+0%)
2,750
+207 (+8.16%)
2,930
+186 (+6.78%)
3,200
+269 (+9.18%)
3,310
+108 (+3.37%)
3,440
+134 (+4.05%)
3,660
3,560  3,760
+215 (+6.24%)
3,850
3,750  3,960
+193 (+5.29%)
4,050
3,940  4,170
+204 (+5.29%)
4,270
4,150  4,390
+214 (+5.29%)
4,490
4,370  4,620
+226 (+5.29%)
4,730
4,600  4,860
+238 (+5.29%)
4,980
4,850  5,120
+250 (+5.29%)
5,250
5,100  5,390
+263 (+5.29%)
5,520
5,370  5,680
+277 (+5.29%)
5,810
5,660  5,980
+292 (+5.29%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Delaware of about 6.49% 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 Delaware assuming that the numbers are following an exponential trend without any slowdown.
46

55
+9 (+19.6%)
61
+6 (+10.9%)
67
+6 (+9.84%)
67
+0 (+0%)
72
+5 (+7.46%)
82
+10 (+13.9%)
89
+7 (+8.54%)
92
+3 (+3.37%)
100
+8 (+8.7%)
106
104  108
+6 (+5.92%)
113
110  115
+7 (+6.49%)
120
117  123
+7 (+6.49%)
128
125  131
+8 (+6.49%)
136
133  139
+8 (+6.49%)
145
142  148
+9 (+6.49%)
154
151  158
+9 (+6.49%)
164
161  168
+10 (+6.49%)
175
171  179
+11 (+6.49%)
186
182  191
+11 (+6.49%)
In United States of America, approx. 0.815% of the population die each year. With a population of roughly 974,000 people in Delaware, that corresponds to about 22 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 Delaware would be approx. 4.3%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Delaware in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Delaware in the previous days.
The graph tries to predict the number of required intensive care units in Delaware. We assume the following:
The graph above and the following table show the course of reported coronavirus infections in Diamond Princess assuming that the numbers are following an exponential trend without any slowdown.
49

49
+0 (+0%)
49
+0 (+0%)
49
+0 (+0%)
49
+0 (+0%)
49
+0 (+0%)
49
+0 (+0%)
49
+0 (+0%)
49
+0 (+0%)
49
+0 (+0%)
49
49  49
+0 (+1.11e14%)
49
49  49
+0 (+0%)
49
49  49
+0 (+0%)
49
49  49
+0 (+0%)
49
49  49
+0 (+0%)
49
49  49
+0 (+0%)
49
49  49
+0 (+0%)
49
49  49
+0 (+0%)
49
49  49
+0 (+0%)
49
49  49
+0 (+0%)
The graph above and the following table show the course of reported deaths by coronavirus in Diamond Princess 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 Diamond Princess 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 Diamond Princess in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Diamond Princess in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in District of Columbia of about 4.47% each day. That corresponds to a doubling of the numbers approx. every 16 days.
The graph above and the following table show the course of reported coronavirus infections in District of Columbia assuming that the numbers are following an exponential trend without any slowdown.
2,200

2,350
+153 (+6.96%)
2,480
+126 (+5.36%)
2,670
+190 (+7.67%)
2,790
+127 (+4.76%)
2,930
+134 (+4.8%)
3,100
+171 (+5.84%)
3,210
+108 (+3.49%)
3,360
+155 (+4.83%)
3,530
+167 (+4.97%)
3,670
3,650  3,700
+147 (+4.16%)
3,840
3,810  3,870
+164 (+4.47%)
4,010
3,980  4,040
+172 (+4.47%)
4,190
4,160  4,220
+179 (+4.47%)
4,380
4,340  4,410
+187 (+4.47%)
4,570
4,540  4,610
+196 (+4.47%)
4,780
4,740  4,810
+204 (+4.47%)
4,990
4,950  5,030
+213 (+4.47%)
5,210
5,170  5,250
+223 (+4.47%)
5,450
5,400  5,490
+233 (+4.47%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in District of Columbia of about 10.8% each day. That corresponds to a doubling of the numbers approx. every 6.8 days.
The graph above and the following table show the course of reported deaths by coronavirus in District of Columbia assuming that the numbers are following an exponential trend without any slowdown.
72

81
+9 (+12.5%)
86
+5 (+6.17%)
91
+5 (+5.81%)
96
+5 (+5.49%)
105
+9 (+9.38%)
112
+7 (+6.67%)
127
+15 (+13.4%)
139
+12 (+9.45%)
153
+14 (+10.1%)
170
167  173
+17 (+11.4%)
189
186  192
+18 (+10.8%)
209
206  213
+20 (+10.8%)
232
228  236
+23 (+10.8%)
257
252  261
+25 (+10.8%)
285
280  290
+28 (+10.8%)
315
310  321
+31 (+10.8%)
350
344  356
+34 (+10.8%)
387
381  394
+38 (+10.8%)
429
422  437
+42 (+10.8%)
In United States of America, approx. 0.815% of the population die each year. With a population of roughly 706,000 people in District of Columbia, that corresponds to about 16 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 District of Columbia would be approx. 6.2%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in District of Columbia in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in District of Columbia in the previous days.
The graph tries to predict the number of required intensive care units in District of Columbia. We assume the following:
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Florida of about 3.25% 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 Florida assuming that the numbers are following an exponential trend without any slowdown.
22,500

23,300
+832 (+3.7%)
24,800
+1,420 (+6.07%)
25,500
+733 (+2.96%)
26,300
+822 (+3.22%)
27,100
+745 (+2.83%)
27,900
+810 (+2.99%)
28,300
+440 (+1.58%)
29,600
+1,340 (+4.73%)
30,500
+885 (+2.99%)
31,500
31,100  31,900
+959 (+3.14%)
32,500
32,100  32,900
+1,020 (+3.25%)
33,600
33,200  34,000
+1,060 (+3.25%)
34,700
34,200  35,100
+1,090 (+3.25%)
35,800
35,300  36,200
+1,130 (+3.25%)
37,000
36,500  37,400
+1,160 (+3.25%)
38,200
37,700  38,600
+1,200 (+3.25%)
39,400
38,900  39,900
+1,240 (+3.25%)
40,700
40,200  41,200
+1,280 (+3.25%)
42,000
41,500  42,500
+1,320 (+3.25%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Florida of about 6.86% each day. That corresponds to a doubling of the numbers approx. every 10 days.
The graph above and the following table show the course of reported deaths by coronavirus in Florida assuming that the numbers are following an exponential trend without any slowdown.
596

668
+72 (+12.1%)
725
+57 (+8.53%)
748
+23 (+3.17%)
774
+26 (+3.48%)
822
+48 (+6.2%)
867
+45 (+5.47%)
893
+26 (+3%)
987
+94 (+10.5%)
1,050
+59 (+5.98%)
1,120
1,080  1,150
+70 (+6.7%)
1,190
1,160  1,230
+77 (+6.86%)
1,270
1,240  1,310
+82 (+6.86%)
1,360
1,320  1,400
+87 (+6.86%)
1,460
1,410  1,500
+93 (+6.86%)
1,550
1,510  1,600
+100 (+6.86%)
1,660
1,610  1,710
+107 (+6.86%)
1,780
1,720  1,830
+114 (+6.86%)
1,900
1,840  1,950
+122 (+6.86%)
2,030
1,970  2,090
+130 (+6.86%)
In United States of America, approx. 0.815% of the population die each year. With a population of roughly 21,500,000 people in Florida, that corresponds to about 480 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 Florida would be approx. 4.2%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Florida in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Florida in the previous days.
The graph tries to predict the number of required intensive care units in Florida. We assume the following:
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Georgia 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 coronavirus infections in Georgia assuming that the numbers are following an exponential trend without any slowdown.
15,000

15,700
+682 (+4.55%)
17,200
+1,530 (+9.73%)
17,700
+475 (+2.76%)
18,300
+632 (+3.58%)
19,400
+1,110 (+6.04%)
19,900
+474 (+2.44%)
21,200
+1,330 (+6.7%)
21,900
+669 (+3.15%)
22,500
+608 (+2.78%)
23,600
23,100  24,100
+1,110 (+4.91%)
24,600
24,100  25,100
+966 (+4.09%)
25,600
25,100  26,100
+1,010 (+4.09%)
26,600
26,100  27,100
+1,050 (+4.09%)
27,700
27,200  28,300
+1,090 (+4.09%)
28,800
28,300  29,400
+1,130 (+4.09%)
30,000
29,400  30,600
+1,180 (+4.09%)
31,200
30,600  31,900
+1,230 (+4.09%)
32,500
31,900  33,200
+1,280 (+4.09%)
33,900
33,200  34,500
+1,330 (+4.09%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Georgia of about 4.04% each day. That corresponds to a doubling of the numbers approx. every 18 days.
The graph above and the following table show the course of reported deaths by coronavirus in Georgia assuming that the numbers are following an exponential trend without any slowdown.
552

587
+35 (+6.34%)
650
+63 (+10.7%)
673
+23 (+3.54%)
687
+14 (+2.08%)
775
+88 (+12.8%)
798
+23 (+2.97%)
848
+50 (+6.27%)
881
+33 (+3.89%)
899
+18 (+2.04%)
945
926  964
+46 (+5.07%)
983
963  1,000
+38 (+4.04%)
1,020
1,000  1,040
+40 (+4.04%)
1,060
1,040  1,090
+41 (+4.04%)
1,110
1,080  1,130
+43 (+4.04%)
1,150
1,130  1,170
+45 (+4.04%)
1,200
1,170  1,220
+46 (+4.04%)
1,250
1,220  1,270
+48 (+4.04%)
1,300
1,270  1,320
+50 (+4.04%)
1,350
1,320  1,380
+52 (+4.04%)
In United States of America, approx. 0.815% of the population die each year. With a population of roughly 10,600,000 people in Georgia, that corresponds to about 237 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 Georgia would be approx. 5.2%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Georgia in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Georgia in the previous days.
The graph tries to predict the number of required intensive care units in Georgia. We assume the following:
The graph above and the following table show the course of reported coronavirus infections in Grand Princess 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%)
103
+0 (+0%)
103
+0 (+0%)
103
103  103
+0 (+0%)
103
103  103
+0 (+0%)
103
103  103
+0 (+0%)
103
103  103
+0 (+0%)
103
103  103
+0 (+0%)
103
103  103
+0 (+0%)
103
103  103
+0 (+0%)
103
103  103
+0 (+0%)
103
103  103
+0 (+0%)
103
103  103
+0 (+0%)
The graph above and the following table show the course of reported deaths by coronavirus in Grand Princess 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%)
3
+0 (+0%)
3
+0 (+0%)
3
3  3
+0 (+1.11e14%)
3
3  3
+0 (+0%)
3
3  3
+0 (+0%)
3
3  3
+0 (+0%)
3
3  3
+0 (+0%)
3
3  3
+0 (+0%)
3
3  3
+0 (+0%)
3
3  3
+0 (+0%)
3
3  3
+0 (+0%)
3
3  3
+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 Grand Princess 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 Grand Princess in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Grand Princess in the previous days.
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Guam of about 1.31% 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 Guam assuming that the numbers are following an exponential trend without any slowdown.
135

135
+0 (+0%)
136
+1 (+0.741%)
136
+0 (+0%)
136
+0 (+0%)
136
+0 (+0%)
136
+0 (+0%)
136
+0 (+0%)
139
+3 (+2.21%)
141
+2 (+1.44%)
143
141  144
+2 (+1.1%)
144
143  146
+2 (+1.31%)
146
145  148
+2 (+1.31%)
148
147  150
+2 (+1.31%)
150
149  152
+2 (+1.31%)
152
151  154
+2 (+1.31%)
154
153  156
+2 (+1.31%)
156
155  158
+2 (+1.31%)
158
157  160
+2 (+1.31%)
160
159  162
+2 (+1.31%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Guam 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 Guam assuming that the numbers are following an exponential trend without any slowdown.
5

5
+0 (+0%)
5
+0 (+0%)
5
+0 (+0%)
5
+0 (+0%)
5
+0 (+0%)
5
+0 (+0%)
5
+0 (+0%)
5
+0 (+0%)
5
+0 (+0%)
5
5  5
+0 (+7.28e10%)
5
5  5
+0 (+2.91e10%)
5
5  5
+0 (+2.91e10%)
5
5  5
+0 (+2.91e10%)
5
5  5
+0 (+2.91e10%)
5
5  5
+0 (+2.91e10%)
5
5  5
+0 (+2.91e10%)
5
5  5
+0 (+2.91e10%)
5
5  5
+0 (+2.91e10%)
5
5  5
+0 (+2.91e10%)
In United States of America, approx. 0.815% of the population die each year. With a population of roughly 166,000 people in Guam, that corresponds to about 4 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 Guam would be approx. 3.7%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Guam in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Guam in the previous days.
The graph tries to predict the number of required intensive care units in Guam. We assume the following:
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Hawaii of about 0.829% each day. That corresponds to a doubling of the numbers approx. every 84 days.
The graph above and the following table show the course of reported coronavirus infections in Hawaii assuming that the numbers are following an exponential trend without any slowdown.
524

530
+6 (+1.15%)
541
+11 (+2.08%)
574
+33 (+6.1%)
580
+6 (+1.05%)
584
+4 (+0.69%)
586
+2 (+0.342%)
592
+6 (+1.02%)
596
+4 (+0.676%)
601
+5 (+0.839%)
606
605  607
+5 (+0.85%)
611
610  612
+5 (+0.829%)
616
615  617
+5 (+0.829%)
621
620  622
+5 (+0.829%)
626
626  627
+5 (+0.829%)
632
631  633
+5 (+0.829%)
637
636  638
+5 (+0.829%)
642
641  643
+5 (+0.829%)
647
647  648
+5 (+0.829%)
653
652  654
+5 (+0.829%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Hawaii of about 8.19% each day. That corresponds to a doubling of the numbers approx. every 8.8 days.
The graph above and the following table show the course of reported deaths by coronavirus in Hawaii assuming that the numbers are following an exponential trend without any slowdown.
9

9
+0 (+0%)
9
+0 (+0%)
9
+0 (+0%)
10
+1 (+11.1%)
10
+0 (+0%)
10
+0 (+0%)
12
+2 (+20%)
12
+0 (+0%)
13
+1 (+8.33%)
14
13  15
+1 (+9.54%)
15
14  17
+1 (+8.19%)
17
15  18
+1 (+8.19%)
18
17  19
+1 (+8.19%)
20
18  21
+1 (+8.19%)
21
20  23
+2 (+8.19%)
23
21  25
+2 (+8.19%)
25
23  27
+2 (+8.19%)
27
25  29
+2 (+8.19%)
29
27  31
+2 (+8.19%)
In United States of America, approx. 0.815% of the population die each year. With a population of roughly 1,420,000 people in Hawaii, that corresponds to about 32 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 Hawaii would be approx. 2.4%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Hawaii in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Hawaii in the previous days.
The graph tries to predict the number of required intensive care units in Hawaii. We assume the following:
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Idaho of about 2.65% each day. That corresponds to a doubling of the numbers approx. every 26 days.
The graph above and the following table show the course of reported coronavirus infections in Idaho assuming that the numbers are following an exponential trend without any slowdown.
1,470

1,590
+114 (+7.74%)
1,610
+22 (+1.39%)
1,660
+46 (+2.86%)
1,670
+13 (+0.785%)
1,670
+4 (+0.24%)
1,740
+64 (+3.83%)
1,770
+30 (+1.73%)
1,840
+70 (+3.96%)
1,870
+34 (+1.85%)
1,920
1,900  1,940
+53 (+2.84%)
1,970
1,960  1,990
+51 (+2.65%)
2,030
2,010  2,050
+52 (+2.65%)
2,080
2,060  2,100
+54 (+2.65%)
2,140
2,120  2,160
+55 (+2.65%)
2,190
2,170  2,210
+57 (+2.65%)
2,250
2,230  2,270
+58 (+2.65%)
2,310
2,290  2,330
+60 (+2.65%)
2,370
2,350  2,390
+61 (+2.65%)
2,430
2,410  2,460
+63 (+2.65%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Idaho of about 4.19% 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 Idaho assuming that the numbers are following an exponential trend without any slowdown.
39

41
+2 (+5.13%)
41
+0 (+0%)
43
+2 (+4.88%)
44
+1 (+2.33%)
45
+1 (+2.27%)
48
+3 (+6.67%)
51
+3 (+6.25%)
54
+3 (+5.88%)
54
+0 (+0%)
57
55  59
+3 (+6.07%)
60
58  62
+2 (+4.19%)
62
60  64
+3 (+4.19%)
65
63  67
+3 (+4.19%)
67
65  70
+3 (+4.19%)
70
68  73
+3 (+4.19%)
73
71  76
+3 (+4.19%)
76
74  79
+3 (+4.19%)
80
77  82
+3 (+4.19%)
83
80  86
+3 (+4.19%)
In United States of America, approx. 0.815% of the population die each year. With a population of roughly 1,790,000 people in Idaho, that corresponds to about 40 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 Idaho would be approx. 3.4%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Idaho in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Idaho in the previous days.
The graph tries to predict the number of required intensive care units in Idaho. We assume the following:
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Illinois of about 6.15% 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 Illinois assuming that the numbers are following an exponential trend without any slowdown.
24,600

25,700
+1,140 (+4.64%)
27,600
+1,840 (+7.17%)
29,200
+1,580 (+5.74%)
30,400
+1,200 (+4.1%)
31,500
+1,160 (+3.81%)
33,100
+1,550 (+4.91%)
35,100
+2,050 (+6.19%)
36,900
+1,830 (+5.21%)
39,700
+2,720 (+7.37%)
41,900
41,600  42,300
+2,260 (+5.7%)
44,500
44,100  44,900
+2,580 (+6.15%)
47,200
46,800  47,600
+2,740 (+6.15%)
50,100
49,700  50,600
+2,900 (+6.15%)
53,200
52,800  53,700
+3,080 (+6.15%)
56,500
56,000  57,000
+3,270 (+6.15%)
60,000
59,500  60,500
+3,470 (+6.15%)
63,700
63,100  64,200
+3,690 (+6.15%)
67,600
67,000  68,200
+3,910 (+6.15%)
71,700
71,100  72,300
+4,160 (+6.15%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Illinois of about 7.03% each day. That corresponds to a doubling of the numbers approx. every 10 days.
The graph above and the following table show the course of reported deaths by coronavirus in Illinois assuming that the numbers are following an exponential trend without any slowdown.
949

1,070
+123 (+13%)
1,130
+60 (+5.6%)
1,260
+127 (+11.2%)
1,290
+31 (+2.46%)
1,350
+59 (+4.57%)
1,470
+119 (+8.82%)
1,570
+97 (+6.61%)
1,690
+123 (+7.86%)
1,800
+107 (+6.34%)
1,920
1,910  1,940
+130 (+7.23%)
2,060
2,050  2,070
+135 (+7.03%)
2,200
2,190  2,220
+145 (+7.03%)
2,360
2,350  2,370
+155 (+7.03%)
2,530
2,510  2,540
+166 (+7.03%)
2,700
2,690  2,720
+177 (+7.03%)
2,890
2,880  2,910
+190 (+7.03%)
3,100
3,080  3,110
+203 (+7.03%)
3,310
3,290  3,330
+218 (+7.03%)
3,550
3,530  3,570
+233 (+7.03%)
In United States of America, approx. 0.815% of the population die each year. With a population of roughly 12,700,000 people in Illinois, that corresponds to about 283 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 Illinois would be approx. 6.5%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Illinois in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Illinois in the previous days.
The graph tries to predict the number of required intensive care units in Illinois. We assume the following:
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Indiana of about 4.25% 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 coronavirus infections in Indiana assuming that the numbers are following an exponential trend without any slowdown.
8,960

9,540
+582 (+6.5%)
10,200
+612 (+6.41%)
10,600
+487 (+4.8%)
11,200
+570 (+5.36%)
11,700
+477 (+4.25%)
12,100
+409 (+3.5%)
12,400
+341 (+2.82%)
13,000
+601 (+4.83%)
13,700
+642 (+4.92%)
14,200
14,000  14,400
+523 (+3.82%)
14,800
14,600  15,000
+604 (+4.25%)
15,400
15,300  15,600
+630 (+4.25%)
16,100
15,900  16,300
+656 (+4.25%)
16,800
16,600  17,000
+684 (+4.25%)
17,500
17,300  17,700
+713 (+4.25%)
18,200
18,000  18,400
+744 (+4.25%)
19,000
18,800  19,200
+775 (+4.25%)
19,800
19,600  20,000
+808 (+4.25%)
20,700
20,400  20,900
+843 (+4.25%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Indiana of about 5.35% 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 Indiana assuming that the numbers are following an exponential trend without any slowdown.
436

477
+41 (+9.4%)
522
+45 (+9.43%)
545
+23 (+4.41%)
562
+17 (+3.12%)
577
+15 (+2.67%)
635
+58 (+10.1%)
666
+31 (+4.88%)
706
+40 (+6.01%)
741
+35 (+4.96%)
781
778  785
+40 (+5.44%)
823
819  827
+42 (+5.35%)
867
863  871
+44 (+5.35%)
914
909  918
+46 (+5.35%)
963
958  967
+49 (+5.35%)
1,010
1,010  1,020
+52 (+5.35%)
1,070
1,060  1,070
+54 (+5.35%)
1,130
1,120  1,130
+57 (+5.35%)
1,190
1,180  1,190
+60 (+5.35%)
1,250
1,240  1,260
+63 (+5.35%)
In United States of America, approx. 0.815% of the population die each year. With a population of roughly 6,730,000 people in Indiana, that corresponds to about 150 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 Indiana would be approx. 7.3%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Indiana in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Indiana in the previous days.
The graph tries to predict the number of required intensive care units in Indiana. We assume the following:
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Iowa of about 6.66% 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 coronavirus infections in Iowa assuming that the numbers are following an exponential trend without any slowdown.
2,000

2,140
+146 (+7.32%)
2,330
+191 (+8.92%)
2,510
+181 (+7.76%)
2,900
+389 (+15.5%)
3,160
+257 (+8.86%)
3,640
+482 (+15.3%)
3,750
+107 (+2.94%)
3,920
+176 (+4.7%)
4,450
+521 (+13.3%)
4,610
4,390  4,850
+170 (+3.81%)
4,920
4,680  5,170
+307 (+6.66%)
5,250
4,990  5,520
+328 (+6.66%)
5,600
5,330  5,880
+349 (+6.66%)
5,970
5,680  6,280
+373 (+6.66%)
6,370
6,060  6,690
+398 (+6.66%)
6,790
6,460  7,140
+424 (+6.66%)
7,250
6,890  7,620
+452 (+6.66%)
7,730
7,350  8,120
+482 (+6.66%)
8,240
7,840  8,660
+514 (+6.66%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Iowa of about 8.62% each day. That corresponds to a doubling of the numbers approx. every 8.4 days.
The graph above and the following table show the course of reported deaths by coronavirus in Iowa assuming that the numbers are following an exponential trend without any slowdown.
53

60
+7 (+13.2%)
64
+4 (+6.67%)
74
+10 (+15.6%)
75
+1 (+1.35%)
79
+4 (+5.33%)
83
+4 (+5.06%)
90
+7 (+8.43%)
96
+6 (+6.67%)
107
+11 (+11.5%)
115
113  117
+8 (+7.55%)
125
123  127
+10 (+8.62%)
136
133  138
+11 (+8.62%)
147
145  150
+12 (+8.62%)
160
157  163
+13 (+8.62%)
174
171  177
+14 (+8.62%)
189
185  193
+15 (+8.62%)
205
201  209
+16 (+8.62%)
223
219  227
+18 (+8.62%)
242
237  247
+19 (+8.62%)
In United States of America, approx. 0.815% of the population die each year. With a population of roughly 3,160,000 people in Iowa, that corresponds to about 70 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 Iowa would be approx. 4.6%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Iowa in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Iowa in the previous days.
The graph tries to predict the number of required intensive care units in Iowa. We assume the following:
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Kansas of about 11.6% each day. That corresponds to a doubling of the numbers approx. every 6.3 days.
The graph above and the following table show the course of reported coronavirus infections in Kansas assuming that the numbers are following an exponential trend without any slowdown.
1,500

1,620
+111 (+7.38%)
1,730
+115 (+7.12%)
1,820
+91 (+5.26%)
1,910
+84 (+4.61%)
2,050
+143 (+7.51%)
2,160
+116 (+5.66%)
2,330
+167 (+7.72%)
2,720
+390 (+16.7%)
2,960
+238 (+8.75%)
3,320
3,210  3,430
+359 (+12.1%)
3,700
3,580  3,830
+383 (+11.6%)
4,130
3,990  4,270
+428 (+11.6%)
4,610
4,450  4,770
+477 (+11.6%)
5,140
4,970  5,320
+532 (+11.6%)
5,730
5,540  5,930
+594 (+11.6%)
6,390
6,180  6,620
+662 (+11.6%)
7,130
6,890  7,380
+739 (+11.6%)
7,960
7,690  8,230
+824 (+11.6%)
8,880
8,580  9,180
+919 (+11.6%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Kansas of about 2.5% each day. That corresponds to a doubling of the numbers approx. every 28 days.
The graph above and the following table show the course of reported deaths by coronavirus in Kansas assuming that the numbers are following an exponential trend without any slowdown.
71

80
+9 (+12.7%)
82
+2 (+2.5%)
85
+3 (+3.66%)
93
+8 (+9.41%)
102
+9 (+9.68%)
109
+7 (+6.86%)
112
+3 (+2.75%)
113
+1 (+0.893%)
118
+5 (+4.42%)
120
118  122
+2 (+1.82%)
123
121  125
+3 (+2.5%)
126
124  128
+3 (+2.5%)
129
128  131
+3 (+2.5%)
133
131  135
+3 (+2.5%)
136
134  138
+3 (+2.5%)
139
137  141
+3 (+2.5%)
143
141  145
+3 (+2.5%)
146
144  148
+4 (+2.5%)
150
148  152
+4 (+2.5%)
In United States of America, approx. 0.815% of the population die each year. With a population of roughly 2,910,000 people in Kansas, that corresponds to about 65 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 Kansas would be approx. 6.8%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Kansas in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Kansas in the previous days.
The graph tries to predict the number of required intensive care units in Kansas. We assume the following:
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Kentucky of about 5.39% 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 coronavirus infections in Kentucky assuming that the numbers are following an exponential trend without any slowdown.
2,210

2,440
+225 (+10.2%)
2,520
+87 (+3.57%)
2,710
+185 (+7.34%)
2,960
+253 (+9.35%)
3,050
+90 (+3.04%)
3,200
+154 (+5.05%)
3,380
+174 (+5.43%)
3,480
+101 (+2.99%)
3,780
+300 (+8.62%)
3,940
3,850  4,030
+159 (+4.2%)
4,150
4,060  4,250
+212 (+5.39%)
4,370
4,280  4,470
+224 (+5.39%)
4,610
4,510  4,710
+236 (+5.39%)
4,860
4,750  4,970
+248 (+5.39%)
5,120
5,000  5,240
+262 (+5.39%)
5,390
5,270  5,520
+276 (+5.39%)
5,690
5,560  5,820
+291 (+5.39%)
5,990
5,860  6,130
+306 (+5.39%)
6,310
6,170  6,460
+323 (+5.39%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Kentucky of about 5.15% each day. That corresponds to a doubling of the numbers approx. every 14 days.
The graph above and the following table show the course of reported deaths by coronavirus in Kentucky assuming that the numbers are following an exponential trend without any slowdown.
115

129
+14 (+12.2%)
137
+8 (+6.2%)
144
+7 (+5.11%)
146
+2 (+1.39%)
154
+8 (+5.48%)
171
+17 (+11%)
185
+14 (+8.19%)
191
+6 (+3.24%)
200
+9 (+4.71%)
211
207  216
+11 (+5.69%)
222
218  227
+11 (+5.15%)
234
229  239
+11 (+5.15%)
246
241  251
+12 (+5.15%)
258
253  264
+13 (+5.15%)
272
266  277
+13 (+5.15%)
286
280  292
+14 (+5.15%)
300
294  307
+15 (+5.15%)
316
309  323
+15 (+5.15%)
332
325  339
+16 (+5.15%)
In United States of America, approx. 0.815% of the population die each year. With a population of roughly 4,470,000 people in Kentucky, that corresponds to about 100 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 Kentucky would be approx. 7.9%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Kentucky in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Kentucky in the previous days.
The graph tries to predict the number of required intensive care units in Kentucky. We assume the following:
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Louisiana of about 1.72% each day. That corresponds to a doubling of the numbers approx. every 41 days.
The graph above and the following table show the course of reported coronavirus infections in Louisiana assuming that the numbers are following an exponential trend without any slowdown.
22,000

22,500
+581 (+2.65%)
23,100
+586 (+2.6%)
23,600
+462 (+2%)
23,900
+348 (+1.48%)
24,500
+595 (+2.49%)
24,900
+331 (+1.35%)
25,300
+404 (+1.63%)
25,700
+481 (+1.9%)
26,100
+401 (+1.56%)
26,600
26,600  26,600
+461 (+1.76%)
27,100
27,000  27,100
+457 (+1.72%)
27,500
27,500  27,600
+464 (+1.72%)
28,000
28,000  28,000
+472 (+1.72%)
28,500
28,400  28,500
+481 (+1.72%)
29,000
28,900  29,000
+489 (+1.72%)
29,500
29,400  29,500
+497 (+1.72%)
30,000
29,900  30,000
+506 (+1.72%)
30,500
30,400  30,500
+514 (+1.72%)
31,000
31,000  31,000
+523 (+1.72%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Louisiana of about 6% 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 deaths by coronavirus in Louisiana assuming that the numbers are following an exponential trend without any slowdown.
1,100

1,160
+53 (+4.81%)
1,210
+57 (+4.93%)
1,270
+54 (+4.45%)
1,300
+29 (+2.29%)
1,330
+32 (+2.47%)
1,410
+77 (+5.8%)
1,470
+68 (+4.84%)
1,600
+126 (+8.55%)
1,660
+61 (+3.81%)
1,770
1,740  1,800
+111 (+6.68%)
1,880
1,840  1,910
+106 (+6%)
1,990
1,950  2,030
+113 (+6%)
2,110
2,070  2,150
+119 (+6%)
2,240
2,190  2,280
+126 (+6%)
2,370
2,330  2,410
+134 (+6%)
2,510
2,470  2,560
+142 (+6%)
2,660
2,610  2,710
+151 (+6%)
2,820
2,770  2,870
+160 (+6%)
2,990
2,940  3,050
+169 (+6%)
In United States of America, approx. 0.815% of the population die each year. With a population of roughly 4,650,000 people in Louisiana, that corresponds to about 104 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 Louisiana would be approx. 7.2%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Louisiana in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Louisiana in the previous days.
The graph tries to predict the number of required intensive care units in Louisiana. We assume the following:
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Maine of about 2.86% each day. That corresponds to a doubling of the numbers approx. every 25 days.
The graph above and the following table show the course of reported coronavirus infections in Maine assuming that the numbers are following an exponential trend without any slowdown.
770

796
+26 (+3.38%)
827
+31 (+3.89%)
847
+20 (+2.42%)
867
+20 (+2.36%)
875
+8 (+0.923%)
888
+13 (+1.49%)
907
+19 (+2.14%)
937
+30 (+3.31%)
965
+28 (+2.99%)
991
986  996
+26 (+2.72%)
1,020
1,010  1,020
+28 (+2.86%)
1,050
1,040  1,050
+29 (+2.86%)
1,080
1,070  1,080
+30 (+2.86%)
1,110
1,100  1,120
+31 (+2.86%)
1,140
1,140  1,150
+32 (+2.86%)
1,170
1,170  1,180
+33 (+2.86%)
1,210
1,200  1,210
+34 (+2.86%)
1,240
1,240  1,250
+35 (+2.86%)
1,280
1,270  1,280
+36 (+2.86%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Maine of about 9.64% each day. That corresponds to a doubling of the numbers approx. every 7.5 days.
The graph above and the following table show the course of reported deaths by coronavirus in Maine assuming that the numbers are following an exponential trend without any slowdown.
24

27
+3 (+12.5%)
29
+2 (+7.41%)
32
+3 (+10.3%)
34
+2 (+6.25%)
35
+1 (+2.94%)
36
+1 (+2.86%)
39
+3 (+8.33%)
44
+5 (+12.8%)
47
+3 (+6.82%)
52
51  53
+5 (+10.6%)
57
56  58
+5 (+9.64%)
62
61  64
+5 (+9.64%)
68
67  70
+6 (+9.64%)
75
73  77
+7 (+9.64%)
82
81  84
+7 (+9.64%)
90
88  92
+8 (+9.64%)
99
97  101
+9 (+9.64%)
109
106  111
+10 (+9.64%)
119
116  122
+10 (+9.64%)
In United States of America, approx. 0.815% of the population die each year. With a population of roughly 1,340,000 people in Maine, that corresponds to about 30 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 Maine would be approx. 5.7%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Maine in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Maine in the previous days.
The graph tries to predict the number of required intensive care units in Maine. We assume the following:
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Maryland of about 5.51% 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 coronavirus infections in Maryland assuming that the numbers are following an exponential trend without any slowdown.
10,000

10,800
+752 (+7.5%)
11,600
+788 (+7.31%)
12,300
+754 (+6.52%)
12,800
+521 (+4.23%)
13,700
+837 (+6.52%)
14,200
+509 (+3.72%)
14,800
+582 (+4.1%)
15,700
+962 (+6.51%)
16,600
+879 (+5.59%)
17,500
17,300  17,700
+880 (+5.3%)
18,500
18,300  18,600
+963 (+5.51%)
19,500
19,300  19,700
+1,020 (+5.51%)
20,500
20,300  20,800
+1,070 (+5.51%)
21,700
21,500  21,900
+1,130 (+5.51%)
22,900
22,600  23,100
+1,190 (+5.51%)
24,100
23,900  24,400
+1,260 (+5.51%)
25,500
25,200  25,700
+1,330 (+5.51%)
26,900
26,600  27,100
+1,400 (+5.51%)
28,300
28,100  28,600
+1,480 (+5.51%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Maryland of about 6.99% each day. That corresponds to a doubling of the numbers approx. every 10 days.
The graph above and the following table show the course of reported deaths by coronavirus in Maryland assuming that the numbers are following an exponential trend without any slowdown.
311

319
+8 (+2.57%)
334
+15 (+4.7%)
421
+87 (+26%)
461
+40 (+9.5%)
582
+121 (+26.2%)
652
+70 (+12%)
698
+46 (+7.06%)
748
+50 (+7.16%)
798
+50 (+6.68%)
855
853  857
+57 (+7.11%)
914
913  916
+60 (+6.99%)
978
976  980
+64 (+6.99%)
1,050
1,040  1,050
+68 (+6.99%)
1,120
1,120  1,120
+73 (+6.99%)
1,200
1,200  1,200
+78 (+6.99%)
1,280
1,280  1,280
+84 (+6.99%)
1,370
1,370  1,370
+90 (+6.99%)
1,470
1,460  1,470
+96 (+6.99%)
1,570
1,570  1,570
+103 (+6.99%)
In United States of America, approx. 0.815% of the population die each year. With a population of roughly 6,050,000 people in Maryland, that corresponds to about 135 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 Maryland would be approx. 6.9%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Maryland in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Maryland in the previous days.
The graph tries to predict the number of required intensive care units in Maryland. We assume the following:
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Massachusetts of about 7.33% each day. That corresponds to a doubling of the numbers approx. every 9.8 days.
The graph above and the following table show the course of reported coronavirus infections in Massachusetts assuming that the numbers are following an exponential trend without any slowdown.
29,900

32,200
+2,260 (+7.56%)
34,400
+2,220 (+6.9%)
36,400
+1,970 (+5.73%)
38,100
+1,710 (+4.69%)
38,100
+0 (+0%)
41,200
+3,120 (+8.2%)
42,900
+1,750 (+4.24%)
46,000
+3,080 (+7.17%)
51,000
+4,950 (+10.7%)
53,900
52,300  55,500
+2,900 (+5.69%)
57,800
56,100  59,600
+3,950 (+7.33%)
62,100
60,200  64,000
+4,240 (+7.33%)
66,600
64,600  68,700
+4,550 (+7.33%)
71,500
69,400  73,700
+4,880 (+7.33%)
76,700
74,400  79,100
+5,240 (+7.33%)
82,400
79,900  84,900
+5,630 (+7.33%)
88,400
85,800  91,100
+6,040 (+7.33%)
94,900
92,100  97,800
+6,480 (+7.33%)
102,000
98,800  105,000
+6,960 (+7.33%)
Using loglinear regression on the data of the previous days, we could infer an increase of reported deaths by coronavirus in Massachusetts of about 9.13% each day. That corresponds to a doubling of the numbers approx. every 7.9 days.
The graph above and the following table show the course of reported deaths by coronavirus in Massachusetts assuming that the numbers are following an exponential trend without any slowdown.
1,110

1,110
+0 (+0%)
1,250
+137 (+12.4%)
1,400
+159 (+12.8%)
1,710
+302 (+21.5%)
1,710
+0 (+0%)
1,960
+255 (+14.9%)
2,180
+221 (+11.3%)
2,360
+178 (+8.16%)
2,560
+196 (+8.31%)
2,800
2,760  2,850
+248 (+9.7%)
3,060
3,010  3,110
+256 (+9.13%)
3,340
3,290  3,390
+279 (+9.13%)
3,640
3,590  3,700
+305 (+9.13%)
3,980
3,920  4,040
+333 (+9.13%)
4,340
4,270  4,410
+363 (+9.13%)
4,740
4,660  4,810
+396 (+9.13%)
5,170
5,090  5,250
+432 (+9.13%)
5,640
5,560  5,720
+472 (+9.13%)
6,150
6,060  6,250
+515 (+9.13%)
In United States of America, approx. 0.815% of the population die each year. With a population of roughly 6,950,000 people in Massachusetts, that corresponds to about 155 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 Massachusetts would be approx. 7.4%.
The graph aboves shows the mortality depending on different presumed lag.
The graph above shows the daily increase of reported coronavirus infections in Massachusetts in the previous days.
The graph shows the daily increase of reported deaths by coronavirus in Massachusetts in the previous days.
The graph tries to predict the number of required intensive care units in Massachusetts. We assume the following:
Using loglinear regression on the data of the previous days, we could infer an increase of reported coronavirus infections in Michigan of about 3.9% each day. That corresponds to a doubling of the numbers approx. every 18 days.
The graph above and the following table show the course of reported coronavirus infections in Michigan assuming that the numbers are following an exponential trend without any slowdown.
28,100

28,800
+750 (+2.67%)
30,000
+1,210 (+4.21%)
30,800
+768 (+2.56%)
31,400
+633 (+2.06%)