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Elm Laboratory

Consulting and Research

COVID-19 Regional Numbers of Infected People

fit with advanced Gompertz function

Jens Röder

5 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: Sun Aug 15 04:23:03 UTC 2021

Click on the down arrow to open content and click on image to enlarge.
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)
20319000
Brazil
28887412 (±1.6%) 107.363 (±1.1%) 417.09 (±0.8%)
5074725
Argentina
10201882 (±4.5%) 124.493 (±2.0%) 517.32 (±1.8%)
1405333
Bangladesh
2579068 (±11.1%) 150.008 (±4.7%) 534.11 (±5.5%)
493767
Ecuador
655060 (±1.3%) 105.098 (±1.0%) 381.73 (±0.8%)
481631
Bolivia
887876 (±4.9%) 129.143 (±2.5%) 483.57 (±2.4%)
458847
Belarus
565973 (±1.2%) 93.864 (±1.0%) 376.07 (±0.6%)
345637
Dominican Republic
373710 (±0.9%) 86.104 (±1.0%) 320.77 (±0.6%)
270692
Bahrain
701845 (±7.3%) 143.121 (±2.9%) 556.20 (±3.1%)
233797
Armenia
236640 (±0.6%) 66.162 (±1.0%) 296.32 (±0.4%)
185902
Algeria
14381 (±1.4%) 22.825 (±1.1%) 127.96 (±0.4%)
151770
Afghanistan
2599527470 (±584.7%) 492.157 (±29.2%) 2721.78 (±42.6%)
136758
Botswana
12591638 (±63.9%) 235.738 (±6.7%) 1218.28 (±8.4%)
135140
Albania
141167 (±0.5%) 54.715 (±0.9%) 351.66 (±0.2%)
82454
Cameroon
193769 (±8.2%) 135.665 (±3.6%) 521.30 (±3.7%)
52971
Congo (Kinshasa)
1291095 (±200.5%) 56.727 (±14.6%) 320.11 (±18.1%)
44328
Angola
58501 (±3.0%) 100.745 (±2.1%) 423.11 (±1.4%)
34286
Cabo Verde
60662 (±3.7%) 115.098 (±2.0%) 471.56 (±1.6%)
16141
Bahamas
13582 (±1.5%) 73.721 (±2.2%) 309.45 (±0.9%)
14847
Belize
13294 (±0.3%) 41.122 (±1.1%) 312.44 (±0.2%)
13668
Burkina Faso
13987 (±0.7%) 47.889 (±1.7%) 342.89 (±0.3%)
13356
Congo (Brazzaville)
14609 (±1.3%) 88.352 (±1.4%) 315.01 (±0.9%)
13092
New South Wales, Australia
3060 (±0.2%) 7.622 (±1.0%) 85.14 (±0.1%)
11195
Central African Republic
5757 (±0.9%) 29.026 (±4.4%) 165.89 (±1.3%)
9620
Burundi
37000 (±11.9%) 147.579 (±3.1%) 695.20 (±3.1%)
9065
Benin
11172 (±2.3%) 95.160 (±2.0%) 367.64 (±1.3%)
6600
Eritrea
12493 (±4.2%) 108.439 (±2.1%) 511.38 (±1.4%)
5920
Nova Scotia, Canada
2886187893 (±782.0%) 502.391 (±27.8%) 2959.06 (±40.9%)
5052
Guinea-Bissau
4454 (±2.1%) 90.986 (±3.1%) 226.52 (±2.4%)
2566
Bhutan
63035 (±35.3%) 216.628 (±5.3%) 1025.22 (±6.7%)
1447
Newfoundland and Labrador, Canada
325100179 (±686.4%) 547.344 (±26.7%) 3157.98 (±39.4%)
1336
Saint Barthelemy, France
6 (±1.4%) 10.622 (±5.9%) 73.24 (±1.2%)
1178
Sichuan, China
6670815194 (±2570.1%) 1126.712 (±75.7%) 6684.65 (±116.9%)
1059
Western Australia, Australia
1026 (±1.0%) 72.418 (±2.8%) 96.58 (±5.4%)
1008
Anhui, China
995 (±0.0%) 4.619 (±0.5%) 33.05 (±0.1%)
991
Faroe Islands, Denmark
4095308995 (±1993.6%) 817.452 (±59.2%) 4869.01 (±90.6%)
657
Nunavut, Canada
831 (±3.2%) 81.066 (±3.6%) 402.15 (±1.1%)
647
Yukon, Canada
11 (±0.5%) 10.261 (±2.7%) 90.26 (±0.4%)
598
Brunei
795029 (±895.1%) 654.151 (±60.3%) 3338.40 (±90.5%)
440
Liaoning, China
457 (±0.8%) 81.260 (±1.4%) 163.76 (±1.4%)
410
Inner Mongolia, China
387 (±0.6%) 61.084 (±1.8%) 111.07 (±2.4%)
287
Guangxi, China
263 (±0.1%) 5.610 (±1.8%) 31.75 (±0.6%)
235
Tasmania, Australia
232 (±0.1%) 11.180 (±0.8%) 94.20 (±0.1%)
200
Northern Territory, Australia
448 (±6.4%) 139.253 (±2.8%) 520.64 (±3.0%)
147
Guizhou, China
147 (±0.0%) 4.474 (±0.6%) 34.71 (±0.1%)
131
Australian Capital Territory, Australia
117 (±0.2%) 7.992 (±3.0%) 85.08 (±0.3%)
63
Macau, China
48 (±0.4%) 15.768 (±2.9%) 69.56 (±1.3%)

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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