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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: Thu Apr 15 04:23:02 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)
13599994
Brazil
17431461 (±2.7%) 83.628 (±1.9%) 328.48 (±1.4%)
2579000
Argentina
2530365 (±0.6%) 57.661 (±0.8%) 293.00 (±0.3%)
697985
Bangladesh
581332 (±0.6%) 50.495 (±1.2%) 216.29 (±0.5%)
499839
Lebanon
870862 (±1.5%) 75.253 (±0.8%) 407.44 (±0.4%)
347589
Ecuador
411538 (±1.7%) 80.862 (±1.4%) 295.67 (±1.0%)
337635
Belarus
936587 (±8.0%) 115.320 (±2.9%) 463.27 (±3.1%)
284183
Bolivia
314578 (±3.0%) 73.966 (±2.9%) 280.77 (±1.8%)
258637
Dominican Republic
308274 (±1.7%) 73.983 (±1.6%) 287.03 (±1.0%)
225343
Costa Rica
807 (±1.1%) 12.629 (±3.1%) 87.82 (±0.4%)
212130
Egypt
209654 (±2.5%) 69.618 (±2.9%) 236.69 (±1.9%)
204053
Armenia
217593 (±1.5%) 61.359 (±1.7%) 286.00 (±0.7%)
158789
Bahrain
149120 (±1.8%) 68.325 (±2.0%) 258.79 (±1.1%)
128752
Albania
237787 (±2.0%) 78.480 (±1.1%) 405.89 (±0.6%)
118799
Algeria
14381 (±1.4%) 22.825 (±1.1%) 127.96 (±0.4%)
115080
Estonia
677422 (±8.3%) 98.521 (±2.0%) 545.47 (±1.6%)
88445
Cuba
2024 (±0.3%) 12.960 (±0.5%) 104.44 (±0.1%)
61731
Cameroon
139131 (±23.9%) 135.236 (±8.6%) 483.04 (±11.1%)
57364
Afghanistan
51997 (±0.8%) 39.828 (±2.2%) 166.43 (±1.0%)
43444
Botswana
132724 (±3.4%) 95.935 (±1.2%) 483.11 (±0.9%)
28665
Congo (Kinshasa)
53569 (±7.1%) 103.939 (±3.6%) 377.93 (±3.7%)
23697
Angola
22686 (±0.4%) 45.175 (±0.7%) 291.53 (±0.2%)
21106
Guinea
18343 (±1.4%) 61.995 (±2.0%) 216.39 (±1.1%)
19231
Cabo Verde
19425 (±0.8%) 63.725 (±0.9%) 290.77 (±0.4%)
18678
French Polynesia, France
60 (±0.4%) 9.439 (±1.5%) 84.77 (±0.2%)
17549
French Guiana, France
35393744 (±471.3%) 70.170 (±22.4%) 431.41 (±28.1%)
12989
Burkina Faso
21457 (±5.5%) 72.528 (±3.5%) 380.02 (±1.7%)
12513
Belize
13234 (±0.7%) 40.481 (±1.4%) 312.35 (±0.2%)
11277
Guyana
11929 (±1.3%) 64.608 (±1.3%) 317.72 (±0.5%)
10084
Congo (Brazzaville)
10260 (±1.7%) 65.711 (±2.0%) 253.80 (±1.1%)
9505
Bahamas
8673 (±0.3%) 36.545 (±0.8%) 258.76 (±0.1%)
7515
Benin
79870 (±46.5%) 172.749 (±9.0%) 740.09 (±11.9%)
5682
Central African Republic
4939 (±0.2%) 18.818 (±0.9%) 160.88 (±0.1%)
5347
New South Wales, Australia
3060 (±0.2%) 7.622 (±1.0%) 85.14 (±0.1%)
4696
Cambodia
2075102529 (±849.0%) 187.057 (±28.8%) 1223.40 (±36.5%)
3469
Eritrea
8823 (±9.2%) 87.079 (±3.7%) 449.22 (±2.5%)
3262
Burundi
15632452 (±173.1%) 271.594 (±10.0%) 1509.30 (±13.8%)
1781
Nova Scotia, Canada
1908 (±4.6%) 87.347 (±6.9%) 116.91 (±10.4%)
1508
Queensland, Australia
1199 (±0.5%) 13.216 (±5.7%) 82.87 (±1.5%)
1201
Antigua and Barbuda
138186497 (±453.0%) 244.791 (±18.3%) 1482.77 (±24.8%)
1031
Newfoundland and Labrador, Canada
331779757 (±1225.1%) 449.719 (±45.2%) 2614.87 (±67.1%)
994
Anhui, China
992 (±0.0%) 4.601 (±0.4%) 33.03 (±0.1%)
964
Western Australia, Australia
892 (±1.2%) 52.049 (±3.7%) 88.34 (±5.3%)
958
Sichuan, China
6748555127 (±4511.5%) 982.253 (±131.2%) 5833.26 (±203.2%)
927
Bhutan
1190 (±2.7%) 68.909 (±2.4%) 319.52 (±1.1%)
408
Liaoning, China
506 (±2.5%) 90.037 (±2.8%) 188.08 (±3.5%)
395
Nunavut, Canada
380 (±1.8%) 35.431 (±4.4%) 333.76 (±0.4%)
377
Inner Mongolia, China
359 (±0.9%) 52.112 (±2.4%) 101.56 (±2.7%)
270
Guangxi, China
259 (±0.1%) 5.466 (±1.2%) 31.62 (±0.4%)
234
Tasmania, Australia
231 (±0.1%) 11.126 (±0.9%) 94.16 (±0.1%)
219
Brunei
157 (±0.7%) 9.575 (±7.3%) 77.31 (±1.2%)
165
Dominica
377 (±6.6%) 100.678 (±2.8%) 424.53 (±2.5%)
147
Guizhou, China
147 (±0.0%) 4.474 (±0.7%) 34.71 (±0.1%)
123
Australian Capital Territory, Australia
114 (±0.2%) 7.504 (±2.7%) 84.90 (±0.3%)
112
Northern Territory, Australia
813 (±40.1%) 172.700 (±9.6%) 672.42 (±13.4%)
75
Yukon, Canada
11 (±0.5%) 10.261 (±2.7%) 90.26 (±0.4%)
49
Macau, China
47 (±0.3%) 14.590 (±2.2%) 69.30 (±0.9%)
49
Northwest Territories, Canada
149 (±13.4%) 101.744 (±4.8%) 466.25 (±4.2%)
31
Greenland, Denmark
4168419423 (±2351.3%) 728.535 (±52.8%) 4511.86 (±83.1%)

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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