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Elm Laboratory

Consulting and Research

COVID-19 Regional Numbers of Dead People in the US

fit with advanced Gompertz function

Jens Röder

6 minutes read

with Advanced Gompertz Function

All the fits of the data of individual areas are created fully automatically. You may check on the graph (click on the image) if the fit has a sufficient quality, matching the data points as best as possible. There may be some cases, where by too unexpected real data, the fit is not giving a good forecast. So please use common sense to evaluate the data.

Locations or countries with not enough data points or other reasons are skipped and can be found in this list.

The table shows from the left: The maximum last cumulative number, the expected maximum number by fit, graph and location, the T2 also known as doubling time of the exponential growth, the day of turning point of the function.

The turning point is important, showing the turn from purely exponential growth to the process of fading out by limited number of victims.

In the middle of the graph is written the doubling time, that describes the time it needs to double the number of cases. This number is calculated using the logistic function and describes the growth before the turning point, where numbers rise quickly. In a simple picture, the T2 of the Gompertz function describes the later part and the doubling time of the logisitic the first part.

Color code on numbers of dead people: Below 30 , between 30 and 99, between 100 and 499, between 500 and 999, between 1000 and 4999, above 5000.

Color code on locations describe percentages of last number and expected Nmax: Above 95%, between 90% and 95%, between 80% and 89%, between 70% and 79%, between 60% and 69%, when number of cases is above 50 and Nmax is below 50% or the doubling time is below 3.5 days! Above 130%, a reoccurrence of an outbreak is indicated.

The location color code tells you, how well the Covid-19 outbreak has faded out and is under control (greenish). In red and magenta the areas are on fast rise in numbers. The color code in numbers tells you how severe the outbreak was in that region.

Applied maths is explained here.


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Actualisation date: Thu Aug 5 06:04:36 UTC 2021

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Ncurrent
location
Nmax (err) cumulative_dead death rate (err) death_rate deaths_daily T2 (err) dturning_point (err)
10025
Queens, New York
7901 (±1.7%) 10.96 (±4.9%) 17.449 (±13.7%) 62.82 (±10.4%)
156
Clearfield, Pennsylvania
397578 (±451.9%) fiterr (±err) 134.437 (±24.1%) 832.50 (±28.7%)
154
Vermilion, Illinois
106 (±134.9%) fiterr (±err) 35.011 (±96.0%) 329.42 (±16.7%)
143
Phelps, Missouri
479 (±29.2%) 8.56 (±5.2%) 70.377 (±7.3%) 416.79 (±5.1%)
141
Natrona, Wyoming
150 (±8.6%) fiterr (±err) 37.732 (±4.8%) 341.68 (±1.0%)
137
Muskingum, Ohio
765616 (±329.5%) fiterr (±err) 118.474 (±17.5%) 760.42 (±19.8%)
134
Laramie, Wyoming
694 (±47.7%) fiterr (±err) 69.130 (±8.9%) 446.93 (±6.2%)
110
Jim Wells, Texas
360 (±550.4%) 7.99 (±16.0%) 149.891 (±163.2%) 528.01 (±236.3%)
101
Lawrence, Alabama
38 (±0.9%) fiterr (±err) 25.607 (±2.2%) 258.00 (±0.2%)
98
Benton, Minnesota
97 (±63.5%) fiterr (±err) 30.338 (±57.4%) 324.33 (±6.9%)
91
Knox, Indiana
8628967 (±464.4%) fiterr (±err) 198.498 (±18.8%) 1198.11 (±25.5%)
89
Petersburg, Virginia
38 (±4.7%) 11.00 (±22.3%) 4.976 (±43743897619.5%) 34.97 (±250902257950.8%)
86
Barron, Wisconsin
64 (±38.9%) fiterr (±err) 28.106 (±45.5%) 315.01 (±4.3%)
85
Shawano, Wisconsin
65 (±1.3%) fiterr (±err) 25.774 (±2.2%) 299.68 (±0.2%)
83
Geneva, Alabama
50175 (±614.6%) fiterr (±err) 190.460 (±39.1%) 1059.90 (±52.2%)
79
Franklin, Illinois
156 (±8.4%) fiterr (±err) 56.839 (±2.7%) 378.12 (±1.4%)
75
Jessamine, Kentucky
48 (±1.9%) fiterr (±err) 26.983 (±2.5%) 312.31 (±0.2%)
74
Dickinson, Michigan
104 (±78.0%) 2.67 (±12.6%) 37.721 (±53.4%) 327.24 (±10.9%)
73
Harrison, Iowa
117 (±11.1%) fiterr (±err) 49.099 (±5.0%) 348.41 (±1.9%)
70
Fauquier, Virginia
31 (±4.5%) 8.11 (±15.4%) 4.994 (±128633519148.9%) 27.00 (±1191701139067.1%)
69
Newaygo, Michigan
15004277 (±972.5%) fiterr (±err) 174.810 (±34.3%) 1099.72 (±45.7%)
68
Jefferson, New York
24641 (±531.5%) 37.67 (±8.5%) 126.191 (±32.8%) 774.55 (±37.8%)
67
Douglas, Wisconsin
30 (±1.8%) fiterr (±err) 11.095 (±4.5%) 340.87 (±0.1%)
66
Logan, Illinois
14673261 (±724.6%) fiterr (±err) 156.203 (±25.7%) 1001.03 (±33.2%)
65
Fremont, Colorado
1136 (±117.1%) fiterr (±err) 76.633 (±14.0%) 510.07 (±11.1%)
63
Greenup, Kentucky
315 (±1390.4%) fiterr (±err) 146.497 (±247.0%) 648.48 (±320.9%)
61
McLeod, Minnesota
92 (±16.6%) fiterr (±err) 38.470 (±7.2%) 356.11 (±1.8%)
58
Carlton, Minnesota
2850482 (±604.3%) fiterr (±err) 143.299 (±25.5%) 909.79 (±31.6%)
56
Jones, Texas
240 (±32.1%) 6.40 (±15.6%) 57.291 (±6.9%) 416.00 (±3.7%)
55
Roosevelt, Montana
45 (±1.0%) 3.40 (±15.3%) 23.727 (±2.1%) 292.96 (±0.1%)
52
Sierra, New Mexico
3257991 (±631.8%) 10.14 (±38.2%) 110.257 (±25.8%) 753.85 (±28.7%)
51
Amador, California
17 (±1.3%) 11.82 (±34.7%) 12.475 (±9.1%) 224.61 (±0.4%)
50
Warren, Illinois
79 (±6.9%) fiterr (±err) 52.592 (±2.8%) 356.59 (±1.2%)
49
Fulton, Arkansas
17346 (±534.6%) 5.05 (±65.2%) 140.124 (±42.3%) 793.86 (±50.8%)
48
Lawrence, South Dakota
127 (±40.1%) 4.23 (±9.4%) 65.295 (±11.7%) 393.17 (±7.3%)
46
Bonner, Idaho
22 (±14.2%) 11.98 (±25.6%) 31.698 (±8.6%) 343.56 (±1.3%)
44
Gilchrist, Florida
108 (±820.0%) 9.28 (±15.0%) 163.559 (±267.1%) 528.03 (±420.8%)
43
Gem, Idaho
54479 (±717.9%) 9.98 (±33.1%) 154.603 (±44.3%) 900.16 (±55.5%)
42
Paulding, Ohio
37 (±4.4%) fiterr (±err) 33.479 (±3.2%) 329.41 (±0.5%)
41
Franklin, Arkansas
40 (±98.5%) 7.63 (±43.6%) 32.346 (±64.9%) 338.30 (±10.1%)
40
Siskiyou, California
11 (±4.5%) 15.89 (±31.8%) 19.696 (±5.6%) 336.32 (±0.3%)
39
Grant, Arkansas
12128 (±426.6%) fiterr (±err) 163.169 (±31.0%) 908.61 (±39.4%)
38
Bosque, Texas
202 (±1237.8%) 11.20 (±16.9%) 168.415 (±260.3%) 669.21 (±371.7%)
37
Garrard, Kentucky
4 (±1.6%) fiterr (±err) 31.118 (±5.3%) 232.83 (±0.7%)
36
Marion, Arkansas
155640 (±623.8%) fiterr (±err) 113.131 (±34.0%) 733.95 (±37.4%)
35
Jefferson, Iowa
2135779 (±1208.3%) fiterr (±err) 115.788 (±49.3%) 780.26 (±56.2%)
34
Greene, Illinois
36 (±4.0%) fiterr (±err) 38.037 (±4.4%) 288.64 (±0.8%)
33
Montgomery, Arkansas
108 (±58.8%) fiterr (±err) 96.938 (±14.3%) 463.06 (±14.2%)
32
Stone, Arkansas
25 (±8.7%) fiterr (±err) 24.761 (±21.2%) 266.07 (±1.9%)
31
Morgan, Georgia
11 (±4.0%) fiterr (±err) 41.437 (±4.2%) 282.28 (±0.9%)
30
Day, South Dakota
53 (±16.5%) 4.60 (±17.6%) 41.476 (±6.6%) 360.95 (±1.9%)
29
De Witt, Illinois
313337 (±766.1%) fiterr (±err) 176.329 (±39.1%) 1039.48 (±51.2%)
28
Moultrie, Illinois
1047 (±117.4%) fiterr (±err) 125.192 (±14.5%) 657.65 (±16.7%)
27
Newton, Arkansas
26 (±6.9%) fiterr (±err) 46.305 (±14.3%) 206.23 (±2.7%)
26
Chaffee, Colorado
21 (±1.4%) fiterr (±err) 15.402 (±12.7%) 80.52 (±5.5%)
25
Washington, Illinois
16174 (±402.5%) fiterr (±err) 82.174 (±29.3%) 575.91 (±25.8%)
24
Prairie, Arkansas
2346 (±194.3%) 9.75 (±29.8%) 126.975 (±19.4%) 701.68 (±22.3%)
23
Prowers, Colorado
25 (±9.7%) fiterr (±err) 32.892 (±10.4%) 317.95 (±1.1%)
22
Howard, Iowa
46 (±57.4%) fiterr (±err) 82.978 (±21.8%) 379.13 (±18.0%)
21
Avery, North Carolina
19 (±5.3%) 4.69 (±33.7%) 24.858 (±5.2%) 333.70 (±0.4%)
20
Warren, Georgia
11 (±17.9%) fiterr (±err) 54.754 (±20.2%) 248.20 (±6.4%)
19
Macon, Missouri
7042 (±427.8%) fiterr (±err) 103.286 (±31.9%) 654.68 (±32.7%)
18
Sharkey, Mississippi
20 (±9.4%) 6.06 (±19.1%) 48.935 (±15.2%) 218.54 (±3.3%)
17
Clearwater, Minnesota
15 (±3.2%) fiterr (±err) 23.178 (±4.0%) 326.70 (±0.2%)
16
Madison, Virginia
6 (±5.8%) 14.88 (±14.9%) 4.974 (±97817707997.4%) 53.91 (±285754135781.1%)

For countries in this list, the number of dead people must be at minimum 13 people. In the list of numbers of a country, there must be at least 7 different numbers. The turning point is guessed by using half the maximum number and then looks for the first value above that value. If the turning point is at the last value, the fit is omitted.

Locations or countries with not enough data points or other reasons are skipped and can be found in this list.


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