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: Thu Apr 15 04:23:02 UTC 2021
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| 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.