Elm LaboratoryCOVID-19 Regional Numbers of Dead 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 dead people: Below 30 , between 30 and 99, between 100 and 499, between 500 and 999, between 1000 and 4999, above 5000.
Color code on locations describe 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 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 Aug 5 04:17:37 UTC 2021
| Ncurrent location |
Nmax (err) | cumulative_dead | death rate (err) | death_rate | deaths_daily | T2 (err) | dturning_point (err) |
| 1047031 South America, South America | 1836917 (±3.8%) |  | 5.88 (±2.3%) |  |  | 124.779 (±2.0%) | 460.94 (±2.0%) |
| 925498 North America, North America | 1055205 (±0.9%) |  | 2.95 (±3.1%) |  |  | 83.808 (±1.0%) | 305.65 (±0.6%) |
| 558432 Brazil | 1331067 (±5.9%) |  | 5.69 (±2.4%) |  |  | 140.760 (±2.4%) | 537.23 (±2.6%) |
| 492324 South Asia, South Asia | 7481169 (±45.3%) |  | 3.19 (±2.0%) |  |  | 219.346 (±7.8%) | 971.33 (±10.3%) |
| 217389 East Europe, East Europe | 271725 (±0.6%) |  | 4.11 (±5.8%) |  |  | 91.801 (±0.5%) | 405.60 (±0.3%) |
| 208492 Middle East, Middle East | 236737 (±0.8%) |  | 3.18 (±3.9%) |  |  | 89.521 (±0.7%) | 339.39 (±0.5%) |
| 172515 Africa, Africa | 185922 (±1.1%) |  | 5.61 (±2.4%) |  |  | 89.223 (±1.0%) | 362.60 (±0.6%) |
| 128115 Italy | 178929 (±2.7%) |  | fiterr (±err) |  |  | 106.540 (±2.2%) | 342.85 (±2.0%) |
| 106447 Argentina | 136372 (±3.5%) |  | 4.50 (±2.1%) |  |  | 101.864 (±2.4%) | 412.16 (±1.7%) |
| 31644 Ecuador | 25874 (±2.4%) |  | 8.94 (±1.9%) |  |  | 89.652 (±2.8%) | 287.58 (±2.0%) |
| 26569 Canada, Canada | 32580 (±2.1%) |  | 2.09 (±23.7%) |  |  | 97.560 (±2.2%) | 295.98 (±1.8%) |
| 25251 Belgium | 28595 (±1.6%) |  | 1.49 (±54.2%) |  |  | 88.019 (±2.2%) | 236.71 (±1.7%) |
| 23575 Pakistan | 41485 (±4.4%) |  | 4.77 (±2.0%) |  |  | 126.604 (±2.4%) | 449.98 (±2.4%) |
| 21397 Bangladesh | 43046 (±11.9%) |  | 4.30 (±2.0%) |  |  | 151.803 (±4.7%) | 555.44 (±5.5%) |
| 18865 Iraq | 16077 (±0.6%) |  | 3.09 (±3.4%) |  |  | 54.569 (±1.3%) | 238.53 (±0.5%) |
| 18225 Bulgaria | 20246 (±0.7%) |  | fiterr (±err) |  |  | 64.900 (±1.0%) | 378.86 (±0.3%) |
| 17882 Bolivia | 16721 (±1.5%) |  | 8.18 (±2.0%) |  |  | 76.620 (±2.1%) | 283.93 (±1.1%) |
| 12541 Slovakia | 13152 (±0.2%) |  | 4.30 (±2.6%) |  |  | 47.573 (±0.5%) | 397.41 (±0.1%) |
| 10742 Austria | 7865001004 (±1316.1%) |  | fiterr (±err) |  |  | 555.405 (±45.9%) | 3260.35 (±68.4%) |
| 10071 Jordan | 11625 (±1.4%) |  | 2.13 (±2.7%) |  |  | 70.731 (±1.6%) | 392.04 (±0.5%) |
| 9689 Bosnia and Herzegovina | 11861 (±1.1%) |  | 4.38 (±3.6%) |  |  | 77.580 (±1.2%) | 388.52 (±0.4%) |
| 9077 Kazakhstan | 17850 (±6.9%) |  | 3.40 (±4.6%) |  |  | 124.946 (±3.2%) | 501.19 (±3.0%) |
| 6804 Afghanistan | 22954623 (±444.0%) |  | 13.03 (±1.5%) |  |  | 429.737 (±26.8%) | 2302.89 (±38.6%) |
| 5494 North Macedonia | 7040 (±1.2%) |  | 2.51 (±9.4%) |  |  | 84.610 (±1.2%) | 383.60 (±0.6%) |
| 5034 Azerbaijan | 5627 (±1.0%) |  | 3.74 (±5.5%) |  |  | 71.227 (±1.3%) | 358.25 (±0.5%) |
| 4621 Armenia | 5023 (±0.8%) |  | 4.03 (±3.7%) |  |  | 75.707 (±1.0%) | 322.40 (±0.4%) |
| 4395 Ethiopia | 5914 (±2.3%) |  | 2.30 (±5.4%) |  |  | 96.373 (±1.8%) | 387.42 (±1.2%) |
| 4370 Algeria | 3849 (±0.7%) |  | 4.07 (±2.9%) |  |  | 74.841 (±1.0%) | 245.43 (±0.6%) |
| 3968 Dominican Republic | 3882 (±0.8%) |  | 2.26 (±3.3%) |  |  | 72.780 (±1.4%) | 245.34 (±0.8%) |
| 3483 Belarus | 4901 (±1.2%) |  | 2.73 (±3.5%) |  |  | 109.554 (±0.8%) | 406.61 (±0.7%) |
| 2993 Cuba | 3449063256 (±418.8%) |  | 2.04 (±29.3%) |  |  | 344.458 (±13.1%) | 2141.52 (±18.4%) |
| 2550 Denmark | 3266 (±2.8%) |  | fiterr (±err) |  |  | 90.455 (±2.9%) | 325.06 (±1.9%) |
| 2457 Albania | 2762 (±0.7%) |  | 2.15 (±4.6%) |  |  | 66.563 (±1.0%) | 349.35 (±0.3%) |
| 2328 Alberta, Canada | 2356 (±0.7%) |  | fiterr (±err) |  |  | 52.764 (±1.4%) | 347.39 (±0.3%) |
| 2163 Nigeria | 2275 (±1.2%) |  | 2.45 (±3.2%) |  |  | 76.154 (±1.9%) | 247.85 (±1.1%) |
| 1653 Botswana | 2662 (±4.7%) |  | 3.07 (±5.9%) |  |  | 90.333 (±2.3%) | 524.77 (±1.1%) |
| 1631 Montenegro | 1864 (±0.6%) |  | 1.40 (±6.9%) |  |  | 70.330 (±0.7%) | 369.08 (±0.2%) |
| 1407 Australia - Oceania, Australia - Oceania | 1036 (±0.6%) |  | 6.47 (±3.5%) |  |  | 29.437 (±2.6%) | 219.81 (±0.5%) |
| 1384 Bahrain | 152572572 (±367.6%) |  | 1.42 (±3.0%) |  |  | 437.827 (±15.1%) | 2535.52 (±21.8%) |
| 1334 Cameroon | 38206675 (±528.4%) |  | 4.07 (±2.4%) |  |  | 498.557 (±25.6%) | 2769.46 (±37.5%) |
| 1045 Congo (Kinshasa) | 1109 (±1.7%) |  | 6.11 (±2.5%) |  |  | 89.403 (±1.8%) | 327.52 (±1.1%) |
| 1022 Angola | 1365 (±3.5%) |  | 3.89 (±2.6%) |  |  | 105.369 (±2.3%) | 421.23 (±1.7%) |
| 827 Mongolia | 1447 (±3.3%) |  | 1.75 (±46.2%) |  |  | 58.100 (±1.8%) | 544.42 (±0.4%) |
| 799 Norway | 1209 (±3.8%) |  | 3.94 (±4.1%) |  |  | 119.392 (±2.8%) | 353.94 (±3.0%) |
| 560 Haiti | 1403 (±40.5%) |  | 5.38 (±1.4%) |  |  | 195.980 (±14.6%) | 637.64 (±21.6%) |
| 534 Mali | 797 (±3.0%) |  | 6.91 (±3.9%) |  |  | 107.877 (±2.2%) | 389.36 (±1.8%) |
| 338 Belize | 328 (±0.2%) |  | 3.27 (±4.5%) |  |  | 35.607 (±0.7%) | 324.66 (±0.1%) |
| 298 Cabo Verde | 490 (±3.4%) |  | 1.91 (±4.4%) |  |  | 112.232 (±2.0%) | 445.71 (±1.6%) |
| 291 Bahamas | 234 (±1.0%) |  | 3.60 (±2.6%) |  |  | 58.589 (±2.0%) | 275.75 (±0.7%) |
| 234 Guinea | 745 (±16.4%) |  | 2.18 (±3.5%) |  |  | 183.646 (±5.2%) | 670.48 (±6.9%) |
| 227 Gambia | 173 (±0.9%) |  | 4.98 (±1.6%) |  |  | 51.613 (±2.4%) | 245.55 (±0.9%) |
| 222 Maldives | 100431168 (±574.7%) |  | 1.61 (±7.3%) |  |  | 437.545 (±20.4%) | 2601.39 (±29.6%) |
| 196 Niger | 261 (±3.6%) |  | 2.61 (±62.9%) |  |  | 103.515 (±3.4%) | 310.88 (±2.9%) |
| 189 French Guiana, France | 411 (±20.4%) |  | 1.26 (±4.0%) |  |  | 165.441 (±8.1%) | 564.74 (±10.6%) |
| 178 Congo (Brazzaville) | 168 (±1.2%) |  | 2.14 (±3.3%) |  |  | 76.562 (±1.9%) | 242.82 (±1.1%) |
| 174 Chad | 237 (±3.3%) |  | 3.42 (±19.7%) |  |  | 118.375 (±3.3%) | 275.84 (±3.6%) |
| 169 Burkina Faso | 259 (±4.4%) |  | 1.79 (±17.7%) |  |  | 120.392 (±3.3%) | 354.41 (±3.5%) |
| 156 Djibouti | 1438381 (±743.6%) |  | 2.84 (±3.9%) |  |  | 506.272 (±41.7%) | 2727.58 (±61.0%) |
| 155 Togo | 376853699 (±1166.8%) |  | 2.39 (±8.3%) |  |  | 645.051 (±36.0%) | 3841.89 (±54.5%) |
| 154 French Polynesia, France | 143 (±0.1%) |  | 1.02 (±13.0%) |  |  | 34.088 (±0.4%) | 318.93 (±0.1%) |
| 129 Martinique, France | 38563173 (±530.8%) |  | fiterr (±err) |  |  | 546.965 (±19.5%) | 3184.59 (±29.0%) |
| 128 Andorra | 161 (±2.3%) |  | fiterr (±err) |  |  | 107.212 (±2.5%) | 251.58 (±2.6%) |
| 123 Equatorial Guinea | 102 (±0.8%) |  | 2.09 (±2.4%) |  |  | 36.547 (±3.0%) | 182.05 (±1.0%) |
| 108 Benin | 150 (±4.1%) |  | 1.88 (±18.4%) |  |  | 106.829 (±3.2%) | 363.66 (±2.7%) |
| 98 Central African Republic | 74 (±1.1%) |  | 1.84 (±5.2%) |  |  | 25.649 (±5.9%) | 174.52 (±1.3%) |
| 93 Nova Scotia, Canada | 70 (±0.6%) |  | 6.32 (±2.0%) |  |  | 14.613 (±5.3%) | 119.37 (±0.9%) |
| 89 Saint Lucia | 87 (±0.4%) |  | 0.83 (±40.1%) |  |  | 42.234 (±0.9%) | 418.93 (±0.1%) |
| 78 Guinea-Bissau | 77 (±1.9%) |  | 3.26 (±3.2%) |  |  | 87.477 (±2.8%) | 244.68 (±1.8%) |
| 71 New South Wales, Australia | 53 (±0.3%) |  | 1.60 (±2.7%) |  |  | 17.012 (±1.8%) | 100.43 (±0.5%) |
| 48 Barbados | 18331187 (±1072.9%) |  | 2.79 (±33.8%) |  |  | 514.669 (±39.7%) | 3003.71 (±58.5%) |
| 46 New Brunswick, Canada | 53 (±1.2%) |  | 2.34 (±39.8%) |  |  | 66.766 (±1.4%) | 403.98 (±0.4%) |
| 22 Henan, China | 22 (±0.1%) |  | 1.75 (±1.3%) |  |  | 5.513 (±0.9%) | 42.12 (±0.2%) |
For countries in this list, the number of dead 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.