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Elm Laboratory

Consulting and Research

COVID-19 Regional Numbers of Dead People

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: Sun Aug 15 04:17:24 UTC 2021

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Ncurrent
location
Nmax (err) cumulative_dead death rate (err) death_rate deaths_daily T2 (err) dturning_point (err)
1062641
South America, South America
1805616 (±3.4%) 5.85 (±2.3%) 123.853 (±1.9%) 457.11 (±1.8%)
940090
North America, North America
1047422 (±0.8%) 2.91 (±3.0%) 83.298 (±1.0%) 304.27 (±0.6%)
567862
Brazil
1230322 (±4.9%) 5.61 (±2.4%) 136.868 (±2.2%) 519.64 (±2.3%)
502121
South Asia, South Asia
4400004 (±30.5%) 3.19 (±2.0%) 199.790 (±6.4%) 856.97 (±8.1%)
225663
East Europe, East Europe
278892 (±0.6%) 3.96 (±5.8%) 93.265 (±0.5%) 409.98 (±0.3%)
215962
Middle East, Middle East
240270 (±0.7%) 3.16 (±3.8%) 90.500 (±0.7%) 342.25 (±0.4%)
181879
Africa, Africa
197583 (±1.3%) 5.74 (±2.4%) 92.978 (±1.1%) 373.72 (±0.7%)
128379
Italy
171979 (±2.4%) fiterr (±err) 103.882 (±2.1%) 333.73 (±1.8%)
108815
Argentina
146695 (±3.5%) 4.48 (±2.1%) 105.918 (±2.3%) 426.36 (±1.7%)
31870
Ecuador
31820 (±4.0%) 8.62 (±1.8%) 106.586 (±3.5%) 335.83 (±3.0%)
26654
Canada, Canada
32019 (±1.9%) 2.03 (±23.2%) 96.247 (±2.1%) 292.01 (±1.6%)
25287
Belgium
28303 (±1.4%) 1.38 (±55.4%) 87.114 (±2.1%) 234.44 (±1.6%)
24339
Pakistan
40303 (±3.8%) 4.83 (±2.0%) 124.960 (±2.2%) 443.26 (±2.1%)
23810
Bangladesh
169984 (±36.3%) 4.66 (±2.0%) 216.537 (±7.6%) 895.47 (±10.3%)
19541
Iraq
16416 (±0.6%) 3.14 (±3.4%) 56.554 (±1.4%) 241.32 (±0.6%)
18332
Bulgaria
20013 (±0.7%) fiterr (±err) 64.156 (±1.0%) 377.58 (±0.2%)
18152
Bolivia
17406 (±1.6%) 8.12 (±2.1%) 79.990 (±2.1%) 291.50 (±1.2%)
12544
Slovakia
13074 (±0.2%) 4.25 (±2.6%) 47.192 (±0.5%) 397.04 (±0.1%)
10791
Kazakhstan
20508 (±7.4%) 4.00 (±4.5%) 131.468 (±3.2%) 529.40 (±3.1%)
10753
Austria
7794927072 (±1368.2%) fiterr (±err) 572.917 (±47.9%) 3359.53 (±71.3%)
10187
Jordan
11478 (±1.2%) 2.12 (±2.6%) 70.006 (±1.5%) 390.46 (±0.4%)
9701
Bosnia and Herzegovina
11548 (±1.0%) 4.38 (±3.4%) 75.989 (±1.1%) 384.86 (±0.4%)
7000
Afghanistan
160987727 (±538.3%) 13.15 (±1.6%) 473.174 (±25.8%) 2646.61 (±37.5%)
5532
North Macedonia
6818 (±1.1%) 2.54 (±8.8%) 82.694 (±1.1%) 378.61 (±0.5%)
5125
Azerbaijan
5578 (±0.9%) 3.72 (±5.5%) 70.605 (±1.3%) 357.12 (±0.4%)
4730
Algeria
4030 (±0.8%) 4.34 (±2.9%) 78.976 (±1.3%) 254.13 (±0.8%)
4678
Armenia
5013 (±0.7%) 4.02 (±3.7%) 75.573 (±0.9%) 322.10 (±0.4%)
4460
Ethiopia
5763 (±2.0%) 2.31 (±5.3%) 94.869 (±1.7%) 382.58 (±1.1%)
3976
Dominican Republic
3922 (±0.8%) 2.17 (±3.0%) 73.710 (±1.3%) 247.20 (±0.8%)
3842
Cuba
3515509483 (±448.0%) 2.60 (±33.6%) 312.510 (±14.3%) 1964.51 (±19.7%)
3582
Belarus
4983 (±1.1%) 2.76 (±3.5%) 110.535 (±0.8%) 410.13 (±0.6%)
2558
Denmark
3174 (±2.5%) fiterr (±err) 88.599 (±2.7%) 319.71 (±1.7%)
2461
Albania
2724 (±0.6%) 2.13 (±4.4%) 65.617 (±1.0%) 347.74 (±0.3%)
2332
Alberta, Canada
2356 (±0.6%) fiterr (±err) 52.778 (±1.4%) 347.39 (±0.3%)
2211
Nigeria
2275 (±1.1%) 2.47 (±3.2%) 76.187 (±1.8%) 247.92 (±1.1%)
1973
Botswana
5188 (±9.5%) 3.47 (±6.0%) 113.570 (±3.1%) 610.22 (±2.2%)
1640
Montenegro
1837 (±0.5%) 1.41 (±6.6%) 69.409 (±0.7%) 367.25 (±0.2%)
1532
Australia - Oceania, Australia - Oceania
1056 (±0.6%) 6.93 (±3.5%) 31.819 (±2.9%) 219.60 (±0.6%)
1384
Bahrain
152113983 (±375.7%) 1.40 (±3.1%) 448.696 (±15.5%) 2594.61 (±22.4%)
1338
Cameroon
38040788 (±537.0%) 4.02 (±2.4%) 509.996 (±26.2%) 2830.01 (±38.3%)
1082
Angola
1500 (±3.7%) 3.98 (±2.4%) 110.556 (±2.3%) 439.95 (±1.8%)
1050
Congo (Kinshasa)
1143 (±1.7%) 6.03 (±2.5%) 91.508 (±1.7%) 333.46 (±1.1%)
871
Mongolia
1285 (±2.2%) 1.42 (±50.2%) 54.879 (±1.6%) 537.50 (±0.3%)
808
Norway
1149 (±3.2%) 3.91 (±4.1%) 115.782 (±2.6%) 340.82 (±2.6%)
576
Haiti
7235 (±124.4%) 5.45 (±1.4%) 286.644 (±22.8%) 1155.12 (±34.0%)
534
Mali
758 (±2.6%) 6.74 (±3.9%) 104.770 (±2.0%) 378.58 (±1.6%)
344
Belize
329 (±0.2%) 3.29 (±4.4%) 35.787 (±0.7%) 324.74 (±0.1%)
311
Bahamas
248 (±1.1%) 3.79 (±2.6%) 64.189 (±2.1%) 283.14 (±0.8%)
298
Cabo Verde
466 (±2.9%) 1.88 (±4.3%) 109.559 (±1.8%) 435.62 (±1.4%)
277
Guinea
2748 (±42.9%) 2.50 (±3.4%) 245.628 (±8.1%) 1018.55 (±11.4%)
251
Gambia
182 (±1.1%) 5.43 (±1.6%) 57.416 (±2.5%) 252.84 (±1.0%)
222
Maldives
100281532 (±569.3%) 1.57 (±7.3%) 450.302 (±20.3%) 2672.55 (±29.5%)
197
French Guiana, France
1270 (±51.2%) 1.29 (±4.0%) 226.793 (±11.9%) 884.62 (±16.7%)
196
Niger
253 (±3.1%) 2.49 (±62.2%) 101.043 (±3.2%) 302.97 (±2.6%)
188
French Polynesia, France
145 (±0.3%) 1.23 (±12.8%) 34.773 (±1.1%) 319.36 (±0.1%)
184
Martinique, France
38654744 (±548.7%) fiterr (±err) 525.548 (±20.3%) 3064.60 (±30.0%)
179
Congo (Brazzaville)
172 (±1.2%) 2.13 (±3.2%) 78.975 (±1.9%) 247.58 (±1.1%)
175
Mayotte, France
283 (±5.4%) 2.82 (±3.4%) 108.332 (±3.6%) 416.27 (±2.8%)
174
Chad
227 (±2.8%) 3.37 (±18.7%) 114.298 (±3.0%) 263.44 (±3.1%)
170
Burkina Faso
245 (±3.7%) 1.76 (±17.5%) 116.244 (±3.0%) 339.29 (±3.0%)
165
Togo
375897979 (±1106.6%) 2.25 (±8.3%) 652.519 (±34.3%) 3885.19 (±51.8%)
156
Djibouti
1433819 (±730.0%) 2.80 (±3.9%) 516.612 (±41.0%) 2780.72 (±60.1%)
129
Andorra
157 (±2.0%) fiterr (±err) 104.982 (±2.4%) 245.05 (±2.3%)
123
Equatorial Guinea
103 (±0.8%) 2.07 (±2.4%) 38.634 (±3.0%) 182.97 (±1.1%)
113
Benin
147 (±3.6%) 1.94 (±18.1%) 105.564 (±3.0%) 359.32 (±2.4%)
98
Central African Republic
140 (±11.8%) 1.82 (±4.8%) 143.482 (±11.0%) 269.96 (±17.1%)
97
New South Wales, Australia
54 (±0.5%) 2.15 (±2.7%) 17.606 (±3.3%) 100.82 (±1.0%)
95
Saint Lucia
88 (±0.4%) 0.92 (±36.0%) 43.217 (±1.0%) 419.62 (±0.1%)
93
Nova Scotia, Canada
70 (±0.7%) 6.22 (±2.0%) 14.969 (±5.5%) 119.52 (±1.0%)
89
Guinea-Bissau
81 (±2.1%) 3.67 (±3.2%) 92.449 (±2.9%) 255.28 (±2.0%)
48
Barbados
18193384 (±1116.0%) 2.59 (±34.2%) 532.165 (±41.4%) 3101.46 (±61.1%)
46
New Brunswick, Canada
52 (±1.0%) 2.20 (±39.9%) 65.323 (±1.3%) 401.69 (±0.3%)
22
Henan, China
22 (±0.1%) 1.75 (±1.3%) 5.513 (±0.9%) 42.12 (±0.2%)

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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