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Elm Laboratory

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COVID-19 Regional Numbers of Infected 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 infected people: Below 500 , between 500 and 999, between 1000 and 9999, between 10000 and 49999, between 50000 and 99999, above 100000.

The color code on locations describes 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 color code on turning, that is: More than 20 days passed, more than 10 days passed, more than 5 days passed, ahead 5 days, ahead 10 days, more than 10 days ahead.

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: Tue Mar 2 05:26:25 UTC 2021

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Clicking on the name will direct to a page with all images for the country.
Ncurrent
location
Nmax (err) cumulative_inf. infected_daily T2 (err) dturning_point (err)
10551259
Brazil
7884842 (±6.1%) 49.271 (±6.9%) 223.95 (±3.0%)
2107365
Argentina
1911191 (±0.5%) 45.217 (±0.5%) 268.07 (±0.1%)
546216
Bangladesh
494898 (±0.7%) 41.391 (±1.2%) 200.20 (±0.4%)
375050
Lebanon
298757 (±55.1%) 53.189 (±29.9%) 323.84 (±12.7%)
287306
Belarus
1520335 (±113.9%) 139.906 (±24.1%) 568.20 (±33.3%)
286155
Ecuador
246793 (±1.1%) 56.353 (±1.0%) 222.26 (±0.6%)
249010
Bolivia
148953 (±0.2%) 30.401 (±0.5%) 196.71 (±0.1%)
247038
Bulgaria
46483522602 (±458.8%) 201.756 (±17.4%) 1226.68 (±23.8%)
239617
Dominican Republic
163090 (±0.8%) 44.298 (±1.1%) 209.27 (±0.4%)
234537
Azerbaijan
51281982352 (±492.5%) 223.674 (±18.3%) 1345.76 (±25.7%)
204341
Costa Rica
807 (±1.1%) 12.629 (±3.1%) 87.82 (±0.4%)
182424
Egypt
112285 (±3.5%) 26.566 (±11.2%) 162.98 (±2.8%)
172058
Armenia
1006661 (±22.2%) 103.114 (±5.3%) 463.60 (±5.9%)
133504
Alberta, Canada
7442 (±1.4%) 13.650 (±2.3%) 107.84 (±0.3%)
130979
Bosnia and Herzegovina
596601 (±13.9%) 85.268 (±3.6%) 426.18 (±3.2%)
122394
Bahrain
102781 (±5.5%) 49.119 (±6.7%) 208.81 (±3.1%)
107167
Albania
15080990 (±48.5%) 151.380 (±4.4%) 808.54 (±5.5%)
65600
Estonia
15130577714 (±3153.0%) 203.820 (±107.2%) 1259.85 (±148.4%)
55714
Afghanistan
42820 (±1.8%) 24.510 (±6.2%) 152.77 (±1.5%)
55110
Luxembourg
242976022 (±423.2%) 198.162 (±24.4%) 1113.03 (±33.1%)
49779
Cuba
2024 (±0.3%) 12.960 (±0.5%) 104.44 (±0.1%)
35714
Cameroon
23625 (±0.5%) 33.021 (±1.3%) 164.91 (±0.4%)
31859
Manitoba, Canada
297 (±0.6%) 9.098 (±2.6%) 90.60 (±0.3%)
28647
Saskatchewan, Canada
7944993302 (±438.2%) 206.186 (±15.1%) 1269.34 (±21.0%)
28371
Botswana
32323 (±4.3%) 60.636 (±1.9%) 344.35 (±1.0%)
25913
Congo (Kinshasa)
13157 (±1.2%) 36.254 (±2.6%) 170.82 (±0.9%)
20807
Angola
23592 (±2.1%) 45.581 (±1.7%) 293.88 (±0.5%)
18387
French Polynesia, France
60 (±0.4%) 9.439 (±1.5%) 84.77 (±0.2%)
16627
French Guiana, France
35370155 (±471.2%) 70.168 (±22.4%) 431.39 (±28.1%)
15992
Guinea
13821 (±0.6%) 43.558 (±1.0%) 181.44 (±0.4%)
15400
Cabo Verde
15840 (±0.9%) 53.996 (±0.7%) 268.11 (±0.3%)
12293
Belize
140030 (±24.0%) 90.558 (±4.4%) 490.83 (±4.1%)
11982
Burkina Faso
193238723 (±596.5%) 277.010 (±27.3%) 1570.77 (±39.4%)
10866
Andorra
805 (±0.6%) 10.434 (±2.2%) 88.66 (±0.3%)
8820
Congo (Brazzaville)
6055 (±0.7%) 35.313 (±1.4%) 196.56 (±0.4%)
8585
Guyana
7430 (±0.9%) 43.559 (±0.9%) 273.87 (±0.2%)
8519
Bahamas
8461 (±0.6%) 34.898 (±0.9%) 257.50 (±0.1%)
5434
Benin
2999 (±0.8%) 36.448 (±1.7%) 188.23 (±0.5%)
5180
New South Wales, Australia
3060 (±2.4%) 7.622 (±11.1%) 85.14 (±1.1%)
5004
Central African Republic
4855 (±0.1%) 18.203 (±0.6%) 160.52 (±0.1%)
3571
Comoros
640 (±1.4%) 41.349 (±2.7%) 184.15 (±0.9%)
3068
Barbados
91 (±0.7%) 10.968 (±2.6%) 89.81 (±0.4%)
2847
Eritrea
20478 (±98.9%) 147.568 (±15.2%) 671.19 (±20.2%)
2209
Burundi
750 (±1.4%) 43.466 (±2.1%) 206.54 (±0.7%)
1796
Shanghai, China
26919 (±1015.8%) 290.052 (±184.9%) 1081.49 (±312.8%)
1641
Nova Scotia, Canada
1138 (±0.6%) 12.932 (±4.1%) 101.20 (±0.7%)
1331
Queensland, Australia
1124 (±0.3%) 9.226 (±3.4%) 84.22 (±0.5%)
994
Anhui, China
994 (±0.7%) 6.522 (±7.7%) 20.61 (±7.0%)
988
Newfoundland and Labrador, Canada
284 (±0.7%) 8.270 (±6.8%) 88.02 (±0.8%)
913
Western Australia, Australia
703 (±0.9%) 18.376 (±4.7%) 88.02 (±1.7%)
867
Bhutan
750 (±4.4%) 62.335 (±2.8%) 277.81 (±1.8%)
820
Cambodia
3677 (±685.8%) 228.169 (±153.0%) 793.25 (±263.4%)
730
Antigua and Barbuda
161 (±1.8%) 50.696 (±2.3%) 191.99 (±1.2%)
658
Faroe Islands, Denmark
187 (±0.1%) 6.271 (±0.9%) 79.23 (±0.1%)
551
Shaanxi, China
828 (±2749.9%) 43.283 (±1971.1%) 12.26 (±82700.1%)
406
Liaoning, China
2667 (±780.0%) 275.300 (±196.1%) 871.32 (±369.2%)
367
Inner Mongolia, China
647 (±72.6%) 136.061 (±47.5%) 207.42 (±148.8%)
357
Nunavut, Canada
267 (±1.0%) 14.677 (±2.4%) 326.31 (±0.1%)
267
Guangxi, China
257 (±0.7%) 6.483 (±8.5%) 18.42 (±10.1%)
234
Tasmania, Australia
230 (±0.1%) 11.041 (±1.0%) 94.10 (±0.2%)
186
Brunei
144 (±0.2%) 7.577 (±1.8%) 76.54 (±0.2%)
147
Guizhou, China
147 (±0.7%) 6.909 (±7.8%) 21.03 (±7.3%)
142
Dominica
23491268 (±785.8%) 302.019 (±29.6%) 1764.95 (±43.4%)
118
Australian Capital Territory, Australia
112 (±0.2%) 7.104 (±2.2%) 84.75 (±0.2%)
105
Northern Territory, Australia
45275 (±929.3%) 323.656 (±74.9%) 1596.10 (±111.6%)
72
Yukon, Canada
11 (±0.5%) 10.261 (±2.7%) 90.26 (±0.4%)
48
Macau, China
47 (±1.7%) 25.055 (±9.3%) 0.21 (±89896.2%)
47
Northwest Territories, Canada
3705932 (±1188.7%) 301.825 (±46.5%) 1752.85 (±67.9%)

For countries in this list, the number of infected 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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