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.
If you like to support my work:
(by click on image you accept third party cookies, required by paypal)
For old broad desktop overview, follow this link.
Actualisation date: Thu Dec 3 05:24:26 UTC 2020
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) |
| 6386787 Brazil | 6461877 (±0.4%) |  |  | 39.897 (±0.5%) | 207.77 (±0.2%) |
| 1656444 Spain | 7990030812 (±181.9%) |  |  | 206.680 (±10.6%) | 1136.92 (±14.8%) |
| 1643086 United Kingdom | 296353101269 (±553.6%) |  |  | 239.645 (±21.1%) | 1412.20 (±30.4%) |
| 1432570 Argentina | 2136780 (±0.8%) |  |  | 48.362 (±0.5%) | 275.95 (±0.2%) |
| 543975 Indonesia | 1071865 (±1.3%) |  |  | 63.317 (±0.6%) | 301.73 (±0.4%) |
| 467225 Bangladesh | 447485 (±0.5%) |  |  | 36.905 (±0.9%) | 192.34 (±0.3%) |
| 387052 Canada, Canada | 107302 (±0.2%) |  |  | 17.896 (±0.4%) | 112.45 (±0.1%) |
| 285489 Austria | 16147 (±0.4%) |  |  | 9.354 (±1.4%) | 85.10 (±0.2%) |
| 193673 Ecuador | 230505 (±1.5%) |  |  | 53.820 (±1.3%) | 214.68 (±0.8%) |
| 148775 Bulgaria | 47417880896 (±472.1%) |  |  | 164.031 (±17.0%) | 1024.25 (±22.7%) |
| 144810 Bolivia | 147324 (±0.2%) |  |  | 29.889 (±0.4%) | 196.20 (±0.1%) |
| 144302 Dominican Republic | 144821 (±0.5%) |  |  | 39.108 (±0.8%) | 199.77 (±0.2%) |
| 140172 Costa Rica | 807 (±1.1%) |  |  | 12.629 (±3.1%) | 87.82 (±0.4%) |
| 139343 Georgia | 996855 (±6.7%) |  |  | 49.417 (±1.5%) | 374.48 (±0.7%) |
| 138219 Belarus | 109345 (±2.5%) |  |  | 42.984 (±3.7%) | 164.86 (±1.8%) |
| 135967 Armenia | 15761880 (±128.0%) |  |  | 152.573 (±13.3%) | 763.33 (±17.7%) |
| 125602 Azerbaijan | 8156539 (±163.9%) |  |  | 150.073 (±18.2%) | 738.62 (±24.2%) |
| 116303 Egypt | 106805 (±0.3%) |  |  | 23.938 (±0.9%) | 161.19 (±0.2%) |
| 92993 China total, China total | 86857 (±0.2%) |  |  | 6.363 (±2.3%) | 37.46 (±0.7%) |
| 89085 Bosnia and Herzegovina | 1189080475 (±184.6%) |  |  | 184.817 (±9.4%) | 1063.35 (±12.8%) |
| 87137 Bahrain | 96928 (±0.7%) |  |  | 44.098 (±0.9%) | 205.24 (±0.3%) |
| 84169 Kenya | 176859 (±13.4%) |  |  | 69.451 (±5.6%) | 314.95 (±4.5%) |
| 84152 Algeria | 14382 (±1.4%) |  |  | 22.825 (±1.1%) | 127.96 (±0.4%) |
| 67169 Malaysia | 16586689990 (±690.0%) |  |  | 210.522 (±25.3%) | 1266.49 (±35.6%) |
| 62945 North Macedonia | 2869876069 (±316.9%) |  |  | 208.569 (±13.9%) | 1215.57 (±19.5%) |
| 59484 Alberta, Canada | 7442 (±1.4%) |  |  | 13.650 (±2.3%) | 107.84 (±0.3%) |
| 46717 Afghanistan | 40527 (±0.4%) |  |  | 21.604 (±1.3%) | 150.92 (±0.3%) |
| 39014 Albania | 4078476 (±54.2%) |  |  | 134.962 (±5.7%) | 697.37 (±7.1%) |
| 35163 Korea - South | 53193 (±9.7%) |  |  | 81.164 (±5.5%) | 242.50 (±7.0%) |
| 35129 Luxembourg | 8045317598 (±756.4%) |  |  | 237.892 (±28.0%) | 1411.33 (±40.2%) |
| 33894 British Columbia, Canada | 2630 (±0.7%) |  |  | 15.149 (±1.4%) | 93.32 (±0.3%) |
| 24487 Cameroon | 22241 (±0.4%) |  |  | 29.835 (±1.0%) | 161.27 (±0.3%) |
| 17341 Madagascar | 12234629478 (±2500.9%) |  |  | 325.261 (±88.2%) | 1906.53 (±131.2%) |
| 17107 Manitoba, Canada | 297 (±0.6%) |  |  | 9.098 (±2.6%) | 90.60 (±0.3%) |
| 15251 Angola | 52276 (±7.3%) |  |  | 62.423 (±2.4%) | 348.43 (±1.6%) |
| 14559 French Polynesia, France | 60 (±0.4%) |  |  | 9.439 (±1.5%) | 84.77 (±0.2%) |
| 12859 Congo (Kinshasa) | 11337 (±0.4%) |  |  | 27.756 (±1.1%) | 161.35 (±0.3%) |
| 12497 Estonia | 539087265 (±822.5%) |  |  | 247.054 (±35.7%) | 1418.72 (±51.3%) |
| 11240 French Guiana, France | 35310811 (±471.1%) |  |  | 70.163 (±22.4%) | 431.35 (±28.1%) |
| 10816 Cabo Verde | 18675 (±1.5%) |  |  | 58.689 (±0.8%) | 282.95 (±0.5%) |
| 10742 Botswana | 80831 (±17.3%) |  |  | 77.021 (±3.8%) | 413.86 (±3.3%) |
| 9296 Haiti | 8810 (±0.4%) |  |  | 22.649 (±1.2%) | 161.19 (±0.2%) |
| 8745 Saskatchewan, Canada | 1781455461 (±743.2%) |  |  | 223.457 (±27.4%) | 1334.65 (±39.0%) |
| 8381 Cuba | 2024 (±0.3%) |  |  | 12.960 (±0.5%) | 104.44 (±0.1%) |
| 7543 Bahamas | 9314 (±1.0%) |  |  | 37.851 (±1.0%) | 262.56 (±0.2%) |
| 6790 Andorra | 805 (±0.6%) |  |  | 10.434 (±2.2%) | 88.66 (±0.3%) |
| 5854 Belize | 13627 (±5.3%) |  |  | 54.969 (±2.1%) | 324.61 (±1.1%) |
| 5774 Congo (Brazzaville) | 5629 (±0.5%) |  |  | 31.636 (±1.1%) | 192.56 (±0.2%) |
| 4918 Central African Republic | 4833 (±0.1%) |  |  | 18.053 (±0.6%) | 160.43 (±0.1%) |
| 4588 New South Wales, Australia | 3060 (±0.2%) |  |  | 7.622 (±1.0%) | 85.14 (±0.1%) |
| 3015 Benin | 2745 (±0.8%) |  |  | 31.824 (±1.7%) | 182.61 (±0.4%) |
| 2997 Togo | 935 (±8.5%) |  |  | 25.436 (±4.8%) | 144.50 (±2.2%) |
| 2931 Burkina Faso | 6979 (±11.6%) |  |  | 88.372 (±4.9%) | 313.00 (±5.8%) |
| 2503 Curacao, Netherlands | 29 (±3.5%) |  |  | 29.956 (±5.5%) | 101.52 (±2.3%) |
| 2441 Guinea-Bissau | 2344 (±0.5%) |  |  | 26.089 (±1.5%) | 142.05 (±0.5%) |
| 2413 Sierra Leone | 1355 (±2.4%) |  |  | 17.306 (±1.8%) | 138.16 (±0.4%) |
| 2137 Lesotho | 2220 (±0.8%) |  |  | 34.012 (±1.3%) | 228.48 (±0.3%) |
| 1992 Guangdong, China | 1708 (±0.6%) |  |  | 10.822 (±4.5%) | 32.86 (±3.4%) |
| 1650 San Marino | 7243 (±124.3%) |  |  | 147.829 (±33.4%) | 509.36 (±53.1%) |
| 1333 Shanghai, China | 1686 (±5.7%) |  |  | 75.259 (±5.0%) | 156.53 (±7.5%) |
| 1315 Nova Scotia, Canada | 1092 (±0.3%) |  |  | 11.904 (±1.7%) | 100.60 (±0.3%) |
| 1205 Queensland, Australia | 1109 (±0.3%) |  |  | 8.825 (±2.9%) | 84.20 (±0.4%) |
| 992 Anhui, China | 992 (±0.0%) |  |  | 4.597 (±0.4%) | 33.03 (±0.1%) |
| 823 Western Australia, Australia | 667 (±0.7%) |  |  | 13.075 (±4.0%) | 87.58 (±0.9%) |
| 689 Burundi | 628 (±1.1%) |  |  | 35.022 (±1.9%) | 192.73 (±0.5%) |
| 680 Jiangsu, China | 638 (±0.3%) |  |  | 4.898 (±1.2%) | 33.05 (±0.2%) |
| 613 Comoros | 552 (±1.0%) |  |  | 32.841 (±2.1%) | 173.54 (±0.6%) |
| 590 Chongqing, China | 578 (±0.4%) |  |  | 4.917 (±1.2%) | 30.77 (±0.3%) |
| 577 Eritrea | 22080698 (±1001.1%) |  |  | 286.804 (±46.9%) | 1606.60 (±68.4%) |
| 503 Faroe Islands, Denmark | 187 (±0.1%) |  |  | 6.271 (±0.9%) | 79.23 (±0.1%) |
| 500 Shaanxi, China | 246 (±0.2%) |  |  | 4.647 (±0.8%) | 31.37 (±0.2%) |
| 490 Fujian, China | 296 (±0.3%) |  |  | 4.670 (±1.0%) | 30.36 (±0.2%) |
| 414 Bhutan | 522 (±2.1%) |  |  | 50.157 (±1.7%) | 241.96 (±0.8%) |
| 373 Hebei, China | 347 (±0.3%) |  |  | 6.213 (±3.0%) | 34.85 (±0.9%) |
| 339 Newfoundland and Labrador, Canada | 273 (±0.4%) |  |  | 7.450 (±3.8%) | 87.67 (±0.4%) |
| 329 Inner Mongolia, China | 275 (±0.6%) |  |  | 26.819 (±1.9%) | 79.81 (±1.4%) |
| 329 Cambodia | 323 (±1.9%) |  |  | 44.252 (±2.9%) | 142.41 (±1.8%) |
| 300 Tianjin, China | 193 (±0.8%) |  |  | 14.415 (±3.4%) | 36.95 (±3.1%) |
| 289 Liaoning, China | 361 (±4.2%) |  |  | 66.849 (±5.0%) | 112.40 (±7.7%) |
| 285 Cayman Islands, United Kingdom | 3265553 (±2466.4%) |  |  | 377.194 (±136.8%) | 2013.99 (±203.8%) |
| 278 Barbados | 91 (±0.7%) |  |  | 10.968 (±2.6%) | 89.81 (±0.4%) |
| 263 Guangxi, China | 256 (±0.1%) |  |  | 5.345 (±0.7%) | 31.51 (±0.2%) |
| 259 Saint Lucia | 2809576 (±557.3%) |  |  | 125.258 (±29.1%) | 775.57 (±35.0%) |
| 231 Tasmania, Australia | 230 (±0.1%) |  |  | 11.010 (±1.0%) | 94.07 (±0.2%) |
| 221 Shanxi, China | 133 (±0.4%) |  |  | 4.329 (±1.5%) | 31.54 (±0.3%) |
| 182 Gansu, China | 164 (±0.6%) |  |  | 18.162 (±3.4%) | 37.86 (±4.5%) |
| 182 Nunavut, Canada | 190 (±2.4%) |  |  | 9.692 (±3.8%) | 323.57 (±0.1%) |
| 151 Brunei | 143 (±0.2%) |  |  | 7.467 (±1.5%) | 76.48 (±0.2%) |
| 147 Guizhou, China | 147 (±0.1%) |  |  | 4.473 (±0.9%) | 34.71 (±0.2%) |
| 142 Antigua and Barbuda | 144 (±2.1%) |  |  | 45.419 (±2.7%) | 180.36 (±1.3%) |
| 117 Australian Capital Territory, Australia | 111 (±0.2%) |  |  | 6.988 (±2.0%) | 84.70 (±0.2%) |
| 85 Saint Vincent and the Grenadines | 85 (±1.5%) |  |  | 44.461 (±2.0%) | 174.67 (±1.0%) |
| 85 Dominica | 5089827 (±1052.5%) |  |  | 277.114 (±45.2%) | 1582.09 (±65.8%) |
| 53 Northern Territory, Australia | 33 (±1.0%) |  |  | 9.742 (±7.7%) | 87.17 (±1.1%) |
| 47 Yukon, Canada | 11 (±0.5%) |  |  | 10.261 (±2.7%) | 90.26 (±0.4%) |
| 46 Macau, China | 46 (±0.5%) |  |  | 14.339 (±2.7%) | 69.30 (±1.0%) |
| 39 Laos | 19 (±0.5%) |  |  | 7.913 (±2.6%) | 89.11 (±0.3%) |
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.