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 May 13 04:16:32 UTC 2021
| Ncurrent location |
Nmax (err) | cumulative_dead | death rate (err) | death_rate | deaths_daily | T2 (err) | dturning_point (err) |
| 858576 North America, North America | 1271709 (±2.4%) |  | 3.23 (±3.9%) |  |  | 94.398 (±1.6%) | 340.20 (±1.4%) |
| 683688 South America, South America | 818193 (±3.2%) |  | 5.44 (±2.6%) |  |  | 89.635 (±2.3%) | 330.81 (±1.8%) |
| 425540 Brazil | 831639 (±9.2%) |  | 5.69 (±2.4%) |  |  | 120.565 (±4.0%) | 441.03 (±4.5%) |
| 293144 South Asia, South Asia | 216042 (±1.0%) |  | 2.89 (±3.1%) |  |  | 54.888 (±1.7%) | 248.89 (±0.6%) |
| 174729 Middle East, Middle East | 191825 (±0.8%) |  | 3.74 (±3.9%) |  |  | 77.468 (±0.8%) | 303.08 (±0.5%) |
| 163259 East Europe, East Europe | 267154 (±1.5%) |  | 4.65 (±5.4%) |  |  | 90.674 (±0.8%) | 402.58 (±0.6%) |
| 124781 Africa, Africa | 172157 (±1.8%) |  | 4.82 (±2.2%) |  |  | 84.200 (±1.3%) | 348.83 (±0.9%) |
| 123282 Italy | 439905 (±17.0%) |  | fiterr (±err) |  |  | 153.900 (±5.5%) | 553.17 (±7.5%) |
| 68311 Argentina | 60843 (±0.6%) |  | 3.78 (±1.8%) |  |  | 52.368 (±1.1%) | 287.34 (±0.3%) |
| 48544 Ukraine | 91188 (±3.6%) |  | 1.35 (±12.6%) |  |  | 93.627 (±1.7%) | 443.52 (±1.2%) |
| 24693 Canada, Canada | 42354 (±7.4%) |  | 3.49 (±19.0%) |  |  | 114.246 (±4.4%) | 356.61 (±5.0%) |
| 24609 Belgium | 32902 (±4.5%) |  | fiterr (±err) |  |  | 98.762 (±3.9%) | 268.90 (±4.1%) |
| 19286 Ecuador | 17337 (±0.7%) |  | 6.65 (±2.1%) |  |  | 55.615 (±1.3%) | 217.08 (±0.6%) |
| 19210 Pakistan | 27474 (±6.0%) |  | 4.53 (±1.8%) |  |  | 106.053 (±3.6%) | 362.50 (±3.6%) |
| 17104 Bulgaria | 21132 (±2.3%) |  | fiterr (±err) |  |  | 67.861 (±1.8%) | 383.94 (±0.7%) |
| 15834 Iraq | 14210 (±0.4%) |  | 3.20 (±3.5%) |  |  | 44.358 (±0.9%) | 225.07 (±0.3%) |
| 13258 Bolivia | 11923 (±0.8%) |  | 7.28 (±1.6%) |  |  | 48.366 (±1.7%) | 234.05 (±0.6%) |
| 12005 Bangladesh | 10441 (±1.2%) |  | 2.86 (±1.9%) |  |  | 63.798 (±1.7%) | 248.22 (±0.8%) |
| 10413 Austria | 8489467719 (±946.3%) |  | fiterr (±err) |  |  | 418.612 (±32.2%) | 2485.90 (±47.4%) |
| 9151 Jordan | 13706 (±5.5%) |  | 2.46 (±4.0%) |  |  | 78.660 (±3.3%) | 412.63 (±1.7%) |
| 9083 Morocco | 9272 (±0.3%) |  | 1.19 (±11.2%) |  |  | 45.252 (±0.6%) | 294.40 (±0.1%) |
| 8912 Bosnia and Herzegovina | 13758 (±3.9%) |  | 4.80 (±5.7%) |  |  | 85.605 (±2.2%) | 409.81 (±1.3%) |
| 5970 Moldova | 11232 (±2.5%) |  | 3.76 (±3.9%) |  |  | 103.704 (±1.2%) | 422.63 (±1.0%) |
| 5135 North Macedonia | 8390 (±3.6%) |  | 3.03 (±13.4%) |  |  | 94.306 (±1.9%) | 412.25 (±1.4%) |
| 4713 Azerbaijan | 5466 (±2.6%) |  | 4.71 (±4.9%) |  |  | 70.453 (±2.3%) | 355.10 (±1.0%) |
| 4256 Armenia | 4701 (±1.6%) |  | 4.53 (±3.6%) |  |  | 72.157 (±1.5%) | 312.88 (±0.8%) |
| 3911 Ethiopia | 4410 (±3.5%) |  | 2.24 (±6.5%) |  |  | 82.456 (±2.7%) | 338.85 (±1.7%) |
| 3550 Dominican Republic | 3382 (±1.0%) |  | 2.45 (±4.1%) |  |  | 61.348 (±1.6%) | 222.85 (±0.8%) |
| 3382 Kazakhstan | 3176 (±0.6%) |  | 1.42 (±5.1%) |  |  | 45.041 (±1.6%) | 229.66 (±0.5%) |
| 3343 Algeria | 3384 (±0.5%) |  | 3.71 (±3.8%) |  |  | 64.479 (±0.8%) | 223.85 (±0.4%) |
| 2710 Afghanistan | 2599 (±1.0%) |  | 5.91 (±0.9%) |  |  | 58.326 (±1.9%) | 216.23 (±0.9%) |
| 2652 Belarus | 4082 (±2.2%) |  | 2.52 (±3.4%) |  |  | 99.918 (±1.3%) | 370.69 (±1.2%) |
| 2499 Denmark | 8984 (±20.1%) |  | 0.97 (±75.3%) |  |  | 139.261 (±6.8%) | 528.04 (±8.2%) |
| 2420 Albania | 3883 (±1.7%) |  | 2.53 (±5.9%) |  |  | 83.616 (±1.1%) | 393.38 (±0.6%) |
| 2217 Latvia | 2301 (±0.4%) |  | 2.22 (±2.1%) |  |  | 44.950 (±0.6%) | 377.16 (±0.1%) |
| 2148 Oman | 1782 (±0.6%) |  | 1.55 (±4.7%) |  |  | 50.197 (±1.3%) | 243.23 (±0.4%) |
| 2119 Alberta, Canada | 2399 (±1.8%) |  | 1.04 (±67.2%) |  |  | 53.942 (±2.3%) | 348.60 (±0.5%) |
| 1884 Korea - South | 5209 (±11.1%) |  | 1.83 (±71.7%) |  |  | 119.257 (±4.2%) | 478.22 (±4.4%) |
| 1722 Malaysia | 5742 (±10.5%) |  | fiterr (±err) |  |  | 115.756 (±3.3%) | 532.95 (±3.0%) |
| 1545 Montenegro | 2369 (±1.6%) |  | 1.32 (±10.8%) |  |  | 82.133 (±1.0%) | 400.26 (±0.5%) |
| 1152 Cameroon | 47161 (±231.0%) |  | 4.27 (±2.2%) |  |  | 284.617 (±30.3%) | 1263.47 (±44.6%) |
| 1070 Australia - Oceania, Australia - Oceania | 973 (±0.4%) |  | 5.60 (±3.4%) |  |  | 24.560 (±2.1%) | 219.59 (±0.3%) |
| 806 Luxembourg | 1461 (±5.6%) |  | fiterr (±err) |  |  | 96.483 (±3.1%) | 398.75 (±2.4%) |
| 775 Congo (Kinshasa) | 1037 (±3.1%) |  | 5.29 (±2.2%) |  |  | 84.226 (±2.6%) | 314.37 (±1.8%) |
| 767 Norway | 6386 (±44.3%) |  | 4.51 (±3.7%) |  |  | 208.541 (±10.0%) | 795.32 (±14.8%) |
| 755 Cuba | 182634765 (±464.5%) |  | 1.73 (±53.4%) |  |  | 382.920 (±17.1%) | 2257.63 (±24.8%) |
| 753 Somalia | 914412606 (±744.7%) |  | 14.48 (±2.3%) |  |  | 341.454 (±23.8%) | 2082.29 (±34.3%) |
| 751 Botswana | 969 (±1.6%) |  | 2.35 (±9.5%) |  |  | 47.665 (±1.3%) | 428.90 (±0.2%) |
| 697 Bahrain | 705 (±2.9%) |  | 1.11 (±6.9%) |  |  | 80.942 (±2.6%) | 299.78 (±1.8%) |
| 639 Angola | 604 (±0.5%) |  | 3.45 (±3.8%) |  |  | 57.419 (±0.7%) | 289.92 (±0.2%) |
| 506 Mali | 1845 (±18.1%) |  | 8.99 (±3.1%) |  |  | 148.865 (±5.6%) | 569.18 (±7.0%) |
| 417 Malta | 482 (±0.7%) |  | fiterr (±err) |  |  | 58.659 (±0.8%) | 355.46 (±0.2%) |
| 346 Uganda | 359 (±0.6%) |  | fiterr (±err) |  |  | 45.736 (±1.1%) | 305.33 (±0.2%) |
| 323 Belize | 328 (±0.3%) |  | 3.49 (±5.4%) |  |  | 35.485 (±0.9%) | 324.61 (±0.1%) |
| 264 Haiti | 244 (±0.2%) |  | 2.88 (±1.8%) |  |  | 31.151 (±0.7%) | 177.34 (±0.2%) |
| 236 Cabo Verde | 250 (±2.7%) |  | 1.90 (±4.5%) |  |  | 79.794 (±2.2%) | 328.40 (±1.3%) |
| 224 Trinidad and Tobago | 148 (±0.8%) |  | 2.64 (±8.8%) |  |  | 39.434 (±2.2%) | 262.27 (±0.4%) |
| 214 Bahamas | 188 (±0.4%) |  | 3.25 (±3.9%) |  |  | 37.316 (±1.2%) | 258.49 (±0.2%) |
| 175 Gambia | 143 (±0.8%) |  | 4.29 (±2.2%) |  |  | 29.160 (±3.1%) | 229.67 (±0.5%) |
| 172 Chad | 1530 (±68.7%) |  | 4.45 (±25.7%) |  |  | 245.480 (±16.0%) | 887.77 (±25.3%) |
| 170 Mayotte, France | 2657395 (±431.3%) |  | 3.22 (±3.3%) |  |  | 360.962 (±22.1%) | 2007.78 (±31.7%) |
| 164 Burkina Faso | 283851 (±382.0%) |  | 3.06 (±13.4%) |  |  | 407.491 (±26.8%) | 2092.45 (±39.2%) |
| 151 Djibouti | 71 (±1.8%) |  | 3.20 (±4.4%) |  |  | 26.007 (±8.0%) | 156.38 (±2.2%) |
| 148 Congo (Brazzaville) | 135 (±1.0%) |  | 2.04 (±3.8%) |  |  | 56.468 (±1.9%) | 207.79 (±0.9%) |
| 141 French Polynesia, France | 143 (±0.2%) |  | 1.02 (±16.2%) |  |  | 33.952 (±0.4%) | 318.84 (±0.1%) |
| 131 Cambodia | 309 (±10.4%) |  | fiterr (±err) |  |  | 36.808 (±4.2%) | 492.29 (±0.6%) |
| 130 Papua New Guinea | 673579 (±296.8%) |  | fiterr (±err) |  |  | 169.061 (±17.0%) | 1055.01 (±19.6%) |
| 127 Andorra | 294 (±13.5%) |  | 1.13 (±68.1%) |  |  | 152.741 (±6.4%) | 431.43 (±9.9%) |
| 125 Togo | 169 (±3.0%) |  | 4.72 (±5.6%) |  |  | 92.608 (±2.0%) | 341.44 (±1.7%) |
| 112 Equatorial Guinea | 92 (±0.6%) |  | 2.08 (±2.6%) |  |  | 27.189 (±2.6%) | 177.23 (±0.6%) |
| 106 French Guiana, France | 83 (±0.7%) |  | fiterr (±err) |  |  | 36.375 (±2.4%) | 202.28 (±0.7%) |
| 104 Aruba, Netherlands | 129 (±2.7%) |  | fiterr (±err) |  |  | 79.941 (±2.0%) | 352.41 (±1.2%) |
| 101 Benin | 369 (±30.0%) |  | 1.95 (±21.7%) |  |  | 152.919 (±9.5%) | 566.63 (±12.4%) |
| 93 Central African Republic | 65 (±0.6%) |  | 1.77 (±7.2%) |  |  | 15.328 (±4.6%) | 171.82 (±0.5%) |
| 90 San Marino | 248809 (±785.6%) |  | 7.51 (±3.2%) |  |  | 519.137 (±52.3%) | 2663.87 (±78.1%) |
| 87 Martinique, France | 38865441 (±986.9%) |  | 1.38 (±66.9%) |  |  | 530.132 (±34.9%) | 3091.01 (±52.3%) |
| 71 Nova Scotia, Canada | 65 (±0.1%) |  | 5.62 (±1.2%) |  |  | 12.349 (±1.0%) | 118.25 (±0.1%) |
| 67 Guinea-Bissau | 59 (±1.6%) |  | 3.05 (±3.5%) |  |  | 60.715 (±3.2%) | 199.95 (±1.6%) |
| 54 New South Wales, Australia | 53 (±0.2%) |  | 1.28 (±3.0%) |  |  | 16.490 (±1.2%) | 100.07 (±0.3%) |
| 45 Barbados | 19202933 (±861.9%) |  | 4.84 (±31.6%) |  |  | 373.612 (±30.9%) | 2216.96 (±44.9%) |
| 41 New Brunswick, Canada | 71 (±6.1%) |  | 3.52 (±47.8%) |  |  | 80.888 (±3.1%) | 436.57 (±1.7%) |
| 22 Henan, China | 22 (±0.1%) |  | 1.76 (±1.5%) |  |  | 5.514 (±1.0%) | 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.