All the fits of the data of individual continental 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.
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 100 , between 100 and 499, between 500 and 999, between 5000 and 9999, between 10000 and 49999, above 50000.
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 reoccurence 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: Mon Jan 11 05:26:27 UTC 2021
| Ncurrent | Nmax (fit) | cumulative | location | cases daily | t2 (fit) | dturning point (fit) |
| 89690533 | 222273205 (±3.8%) |  | World total |  | 84.660 (±1.4%) | 365.59 (±1.3%) |
| 96690 | 87633 (±0.3%) |  | China total |  | 6.483 (±2.5%) | 37.55 (±0.8%) |
| 89593843 | 220356958 (±3.9%) |  | World without China |  | 84.318 (±1.4%) | 364.61 (±1.3%) |
| 17134984 | 7654622167109 (±386.2%) |  | Europe |  | 247.406 (±13.5%) | 1487.84 (±19.4%) |
| 4699486 | 89911883 (±36.3%) |  | East Europe |  | 135.989 (±5.6%) | 632.21 (±7.2%) |
| 6337564 | 99521456 (±38.3%) |  | Middle East |  | 134.069 (±6.3%) | 613.63 (±8.0%) |
| 3007197 | 2842684 (±1.8%) |  | Africa |  | 52.259 (±1.7%) | 235.95 (±0.8%) |
| 11854675 | 12917170 (±0.3%) |  | South Asia |  | 43.741 (±0.3%) | 246.25 (±0.1%) |
| 1507314 | 2007938 (±1.0%) |  | South-East Asia |  | 62.824 (±0.6%) | 284.84 (±0.4%) |
| 450348 | 74655699 (±210.9%) |  | East Asia |  | 262.101 (±20.5%) | 1243.78 (±30.4%) |
| 31702 | 33917 (±1.5%) |  | Australia - Oceania |  | 43.829 (±2.6%) | 170.83 (±1.2%) |
| 25392643 | 124486782 (±15.9%) |  | North America |  | 107.962 (±3.8%) | 468.58 (±4.5%) |
| 13553125 | 13422736 (±0.7%) |  | South America |  | 48.358 (±0.8%) | 229.59 (±0.3%) |
