Elm LaboratoryCOVID-19 Regional Numbers of Infected People
fit with advanced Gompertz function
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 Jan 17 05:25:38 UTC 2021
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| Ncurrent location |
Nmax (err) | cumulative_inf. | infected_daily | T2 (err) | dturning_point (err) |
| 8393492 Brazil | 7547549 (±0.9%) |  |  | 46.200 (±1.1%) | 220.56 (±0.4%) |
| 1783047 Argentina | 1911191 (±0.5%) |  |  | 45.217 (±0.5%) | 268.07 (±0.1%) |
| 700538 Canada, Canada | 107302 (±0.2%) |  |  | 17.896 (±0.4%) | 112.45 (±0.1%) |
| 526485 Bangladesh | 494898 (±0.7%) |  |  | 41.391 (±1.2%) | 200.20 (±0.4%) |
| 243286 Lebanon | 298734 (±0.9%) |  |  | 53.186 (±0.5%) | 323.84 (±0.2%) |
| 226866 Ecuador | 246793 (±1.1%) |  |  | 56.353 (±1.0%) | 222.26 (±0.6%) |
| 226549 Azerbaijan | 51281961178 (±492.5%) |  |  | 223.673 (±18.3%) | 1345.76 (±25.7%) |
| 221604 Belarus | 789812 (±35.9%) |  |  | 116.718 (±9.9%) | 453.69 (±12.9%) |
| 211503 Bulgaria | 46522464676 (±458.8%) |  |  | 201.704 (±17.4%) | 1226.42 (±23.8%) |
| 188969 Dominican Republic | 163090 (±0.8%) |  |  | 44.298 (±1.1%) | 209.27 (±0.4%) |
| 184187 Costa Rica | 807 (±1.1%) |  |  | 12.629 (±3.1%) | 87.82 (±0.4%) |
| 183589 Bolivia | 148953 (±0.2%) |  |  | 30.401 (±0.5%) | 196.71 (±0.1%) |
| 163972 Armenia | 1006661 (±22.2%) |  |  | 103.114 (±5.3%) | 463.60 (±5.9%) |
| 154620 Egypt | 112265 (±0.5%) |  |  | 26.535 (±1.6%) | 162.99 (±0.4%) |
| 117011 Bosnia and Herzegovina | 596601 (±13.9%) |  |  | 85.268 (±3.6%) | 426.18 (±3.2%) |
| 115370 Alberta, Canada | 7442 (±1.4%) |  |  | 13.650 (±2.3%) | 107.84 (±0.3%) |
| 103381 Algeria | 14382 (±1.4%) |  |  | 22.825 (±1.1%) | 127.96 (±0.4%) |
| 97616 China total, China total | 87633 (±0.3%) |  |  | 6.483 (±2.5%) | 37.55 (±0.8%) |
| 97020 Bahrain | 96714 (±0.5%) |  |  | 44.023 (±0.7%) | 205.05 (±0.2%) |
| 66635 Albania | 15089229 (±48.5%) |  |  | 151.388 (±4.4%) | 808.60 (±5.5%) |
| 60117 British Columbia, Canada | 2630 (±0.7%) |  |  | 15.149 (±1.4%) | 93.32 (±0.3%) |
| 53831 Afghanistan | 42816 (±0.6%) |  |  | 24.490 (±2.1%) | 152.78 (±0.5%) |
| 48630 Luxembourg | 113473659 (±218.4%) |  |  | 187.052 (±13.9%) | 1037.09 (±18.7%) |
| 36096 Estonia | 15114864045 (±567.1%) |  |  | 203.583 (±19.3%) | 1258.53 (±26.7%) |
| 27336 Cameroon | 23625 (±0.5%) |  |  | 33.021 (±1.3%) | 164.91 (±0.4%) |
| 27145 Manitoba, Canada | 297 (±0.6%) |  |  | 9.098 (±2.6%) | 90.60 (±0.3%) |
| 20556 Congo (Kinshasa) | 13157 (±1.2%) |  |  | 36.254 (±2.6%) | 170.82 (±0.9%) |
| 19715 Saskatchewan, Canada | 7944722170 (±438.2%) |  |  | 206.152 (±15.1%) | 1269.16 (±21.0%) |
| 18679 Angola | 23592 (±2.1%) |  |  | 45.581 (±1.7%) | 293.88 (±0.5%) |
| 17635 French Polynesia, France | 60 (±0.4%) |  |  | 9.439 (±1.5%) | 84.77 (±0.2%) |
| 17365 Botswana | 32324 (±4.3%) |  |  | 60.636 (±1.9%) | 344.35 (±1.0%) |
| 17096 Cuba | 2024 (±0.3%) |  |  | 12.960 (±0.5%) | 104.44 (±0.1%) |
| 14526 French Guiana, France | 35340499 (±471.2%) |  |  | 70.165 (±22.4%) | 431.37 (±28.1%) |
| 14065 Guinea | 13821 (±0.6%) |  |  | 43.558 (±1.0%) | 181.44 (±0.4%) |
| 12776 Cabo Verde | 15840 (±0.9%) |  |  | 53.996 (±0.7%) | 268.11 (±0.3%) |
| 11529 Belize | 139999 (±24.0%) |  |  | 90.554 (±4.4%) | 490.81 (±4.1%) |
| 8946 Andorra | 805 (±0.6%) |  |  | 10.434 (±2.2%) | 88.66 (±0.3%) |
| 8882 Burkina Faso | 192985393 (±596.3%) |  |  | 276.991 (±27.3%) | 1570.63 (±39.4%) |
| 8021 Bahamas | 8461 (±0.6%) |  |  | 34.898 (±0.9%) | 257.50 (±0.1%) |
| 7709 Congo (Brazzaville) | 6055 (±0.7%) |  |  | 35.313 (±1.4%) | 196.56 (±0.4%) |
| 6805 Guyana | 7430 (±0.9%) |  |  | 43.559 (±0.9%) | 273.87 (±0.2%) |
| 5057 New South Wales, Australia | 3060 (±0.2%) |  |  | 7.622 (±1.0%) | 85.14 (±0.1%) |
| 4973 Central African Republic | 4855 (±0.1%) |  |  | 18.203 (±0.6%) | 160.52 (±0.1%) |
| 3413 Benin | 2999 (±0.8%) |  |  | 36.448 (±1.7%) | 188.23 (±0.5%) |
| 2084 Guangdong, China | 1762 (±0.6%) |  |  | 14.283 (±4.6%) | 30.58 (±6.3%) |
| 1877 Eritrea | 20407 (±98.8%) |  |  | 147.489 (±15.2%) | 670.71 (±20.2%) |
| 1590 Shanghai, China | 2280 (±7.0%) |  |  | 92.286 (±4.7%) | 221.89 (±7.3%) |
| 1550 Nova Scotia, Canada | 1138 (±0.6%) |  |  | 12.932 (±4.1%) | 101.20 (±0.7%) |
| 1469 Comoros | 640 (±1.4%) |  |  | 41.349 (±2.7%) | 184.15 (±0.9%) |
| 1291 Queensland, Australia | 1124 (±0.3%) |  |  | 9.226 (±3.4%) | 84.22 (±0.5%) |
| 1150 Burundi | 750 (±1.4%) |  |  | 43.466 (±2.1%) | 206.54 (±0.7%) |
| 1036 Barbados | 91 (±0.7%) |  |  | 10.968 (±2.6%) | 89.81 (±0.4%) |
| 1031 Hebei, China | 349 (±0.3%) |  |  | 6.319 (±3.0%) | 34.93 (±1.0%) |
| 993 Anhui, China | 992 (±0.0%) |  |  | 4.598 (±0.4%) | 33.03 (±0.1%) |
| 886 Western Australia, Australia | 696 (±0.8%) |  |  | 15.794 (±4.2%) | 88.74 (±1.3%) |
| 837 Bhutan | 750 (±4.4%) |  |  | 62.335 (±2.8%) | 277.81 (±1.8%) |
| 688 Jiangsu, China | 638 (±0.3%) |  |  | 4.898 (±1.2%) | 33.05 (±0.2%) |
| 648 Faroe Islands, Denmark | 187 (±0.1%) |  |  | 6.271 (±0.9%) | 79.23 (±0.1%) |
| 591 Chongqing, China | 578 (±0.4%) |  |  | 4.917 (±1.2%) | 30.77 (±0.3%) |
| 529 Fujian, China | 296 (±0.3%) |  |  | 4.670 (±1.0%) | 30.36 (±0.2%) |
| 527 Shaanxi, China | 246 (±0.2%) |  |  | 4.647 (±0.8%) | 31.37 (±0.2%) |
| 450 Saint Vincent and the Grenadines | 113 (±2.8%) |  |  | 58.208 (±2.8%) | 207.33 (±1.8%) |
| 436 Cambodia | 356 (±1.7%) |  |  | 48.911 (±2.6%) | 153.81 (±1.7%) |
| 396 Liaoning, China | 348 (±2.7%) |  |  | 64.476 (±3.8%) | 104.66 (±5.3%) |
| 395 Newfoundland and Labrador, Canada | 284 (±0.7%) |  |  | 8.270 (±6.8%) | 88.02 (±0.8%) |
| 366 Inner Mongolia, China | 293 (±0.8%) |  |  | 31.049 (±2.4%) | 83.82 (±2.0%) |
| 327 Tianjin, China | 193 (±0.8%) |  |  | 14.415 (±3.4%) | 36.95 (±3.1%) |
| 266 Guangxi, China | 257 (±0.1%) |  |  | 5.373 (±0.9%) | 31.54 (±0.2%) |
| 266 Nunavut, Canada | 267 (±1.0%) |  |  | 14.677 (±2.4%) | 326.31 (±0.1%) |
| 234 Tasmania, Australia | 230 (±0.1%) |  |  | 11.041 (±1.0%) | 94.10 (±0.2%) |
| 227 Shanxi, China | 133 (±0.4%) |  |  | 4.329 (±1.5%) | 31.54 (±0.3%) |
| 184 Antigua and Barbuda | 161 (±1.8%) |  |  | 50.696 (±2.3%) | 191.99 (±1.2%) |
| 182 Gansu, China | 168 (±0.6%) |  |  | 21.190 (±3.3%) | 35.45 (±6.3%) |
| 174 Brunei | 144 (±0.2%) |  |  | 7.577 (±1.8%) | 76.54 (±0.2%) |
| 147 Guizhou, China | 147 (±0.1%) |  |  | 4.473 (±0.8%) | 34.71 (±0.2%) |
| 118 Australian Capital Territory, Australia | 112 (±0.2%) |  |  | 7.104 (±2.2%) | 84.75 (±0.2%) |
| 109 Dominica | 23488788 (±785.1%) |  |  | 301.861 (±29.6%) | 1764.09 (±43.3%) |
| 93 Northern Territory, Australia | 45137 (±928.6%) |  |  | 323.572 (±74.9%) | 1595.50 (±111.6%) |
| 70 Yukon, Canada | 11 (±0.5%) |  |  | 10.261 (±2.7%) | 90.26 (±0.4%) |
| 46 Macau, China | 46 (±0.5%) |  |  | 14.321 (±2.6%) | 69.29 (±1.0%) |
| 34 Saint Kitts and Nevis | 1325099 (±3356.3%) |  |  | 550.590 (±157.9%) | 3019.10 (±237.9%) |
| 25 Northwest Territories, Canada | 3699558 (±1188.4%) |  |  | 301.802 (±46.5%) | 1752.67 (±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.