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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: Tue Jun 15 04:20:56 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)
17412766
Brazil
25829260 (±2.6%) 101.810 (±1.5%) 395.85 (±1.3%)
4124190
Argentina
5676275 (±4.2%) 98.456 (±2.4%) 411.24 (±1.8%)
826922
Bangladesh
789212 (±1.5%) 72.451 (±2.0%) 261.19 (±1.1%)
438934
Ecuador
627135 (±2.3%) 102.730 (±1.5%) 372.92 (±1.3%)
406954
Bolivia
521397 (±4.2%) 101.514 (±2.8%) 372.08 (±2.4%)
405663
Belarus
630408 (±2.6%) 99.517 (±1.6%) 395.41 (±1.2%)
308650
Dominican Republic
326335 (±1.0%) 77.297 (±1.1%) 296.35 (±0.7%)
258731
Bahrain
1017228 (±18.8%) 160.644 (±5.2%) 640.11 (±6.5%)
223682
Armenia
235512 (±0.9%) 65.818 (±1.3%) 295.68 (±0.5%)
133388
Algeria
14381 (±1.4%) 22.825 (±1.1%) 127.96 (±0.4%)
132459
Albania
153914 (±0.8%) 60.217 (±1.1%) 359.26 (±0.3%)
89861
Afghanistan
60458 (±1.1%) 54.349 (±2.5%) 186.01 (±1.4%)
80090
Cameroon
766046649 (±311.6%) 385.060 (±16.9%) 2115.23 (±24.2%)
59480
Botswana
91600 (±1.2%) 84.549 (±0.7%) 438.91 (±0.4%)
36705
Angola
35691 (±2.2%) 73.464 (±2.3%) 341.57 (±1.0%)
35228
Congo (Kinshasa)
1290831 (±200.5%) 56.726 (±14.6%) 320.10 (±18.1%)
31571
Cabo Verde
90009 (±10.7%) 132.767 (±3.6%) 547.76 (±3.9%)
13459
Burkina Faso
14619 (±1.2%) 51.678 (±2.1%) 344.89 (±0.4%)
12938
Belize
12971 (±0.4%) 39.291 (±1.2%) 311.44 (±0.2%)
12121
Congo (Brazzaville)
12914 (±1.7%) 79.997 (±1.8%) 291.57 (±1.1%)
12092
Bahamas
10174 (±0.8%) 48.882 (±1.8%) 270.20 (±0.5%)
8109
Benin
16975 (±6.8%) 116.114 (±3.2%) 447.82 (±3.1%)
7101
Central African Republic
5311 (±0.7%) 22.247 (±3.7%) 162.69 (±0.8%)
5742
Nova Scotia, Canada
826854289 (±1191.3%) 462.749 (±46.2%) 2679.52 (±68.0%)
5619
New South Wales, Australia
3060 (±0.2%) 7.622 (±1.0%) 85.14 (±0.1%)
5013
Burundi
40702 (±13.5%) 145.814 (±2.9%) 697.11 (±3.1%)
4848
Eritrea
5343 (±2.1%) 68.768 (±1.9%) 394.22 (±0.6%)
3803
Guinea-Bissau
3750 (±1.9%) 72.434 (±3.3%) 191.27 (±2.2%)
1813
Bhutan
3175 (±8.7%) 114.595 (±3.8%) 476.27 (±3.5%)
1375
Newfoundland and Labrador, Canada
331278871 (±707.1%) 480.557 (±26.9%) 2787.21 (±39.7%)
1054
Sichuan, China
6700307474 (±3554.9%) 1070.930 (±104.0%) 6356.04 (±160.9%)
1019
Western Australia, Australia
980 (±1.2%) 65.458 (±3.3%) 92.71 (±5.7%)
1005
Saint Barthelemy, France
6 (±1.4%) 10.622 (±5.9%) 73.24 (±1.2%)
1004
Anhui, China
993 (±0.0%) 4.607 (±0.4%) 33.04 (±0.1%)
756
Faroe Islands, Denmark
4121158296 (±2905.0%) 774.504 (±85.2%) 4616.60 (±130.8%)
657
Nunavut, Canada
2139 (±32.8%) 130.326 (±11.9%) 561.13 (±11.1%)
426
Liaoning, China
467 (±1.2%) 83.319 (±1.9%) 168.59 (±2.0%)
393
Inner Mongolia, China
374 (±0.8%) 57.018 (±2.1%) 106.79 (±2.5%)
275
Guangxi, China
261 (±0.1%) 5.538 (±1.6%) 31.69 (±0.5%)
248
Brunei
3603 (±212.9%) 395.651 (±42.6%) 1425.79 (±72.9%)
234
Tasmania, Australia
232 (±0.1%) 11.156 (±0.8%) 94.18 (±0.1%)
175
Northern Territory, Australia
3601 (±48.7%) 223.954 (±7.9%) 981.48 (±10.9%)
147
Guizhou, China
147 (±0.0%) 4.474 (±0.7%) 34.71 (±0.1%)
124
Australian Capital Territory, Australia
116 (±0.2%) 7.769 (±2.9%) 85.00 (±0.3%)
104
Yukon, Canada
11 (±0.5%) 10.261 (±2.7%) 90.26 (±0.4%)
52
Macau, China
47 (±0.3%) 14.929 (±2.2%) 69.32 (±0.9%)

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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