## COVID-19 Continental 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.

The table shows from the left: The maximum last cumulative number, the expected maximum number by fit, graph and location, the T_{2} 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 T_{2} 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 10000 , between 10000 and 49999, between 50000 and 49999, between 100000 and 499999, between 500000 and 999999, above 1000000.

Color code on locations describe percentages of last number and expected N_{max}:
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 N_{max} 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: Sat Jul 11 04:24:53 UTC 2020

N_{current}location |
N_{max} (err) |
cumulative_inf. | infected_daily | T_{2} (err) |
d_{turning_point} (err) |

12268518 World total | 20307274 (±4.0%) | 35.453 (±2.0%) | 160.61 (±1.5%) | ||

12183526 World without China | 19718484 (±4.1%) | 34.778 (±2.2%) | 159.59 (±1.5%) | ||

3673098 North America | 4166868 (±3.1%) | 28.545 (±2.5%) | 136.17 (±1.2%) | ||

2652216 South America | 5806629 (±1.0%) | 31.122 (±0.4%) | 181.18 (±0.2%) | ||

1352564 Europe | 1256937 (±0.4%) | 14.433 (±1.3%) | 93.39 (±0.3%) | ||

1268121 South Asia | 4271114 (±1.0%) | 35.519 (±0.3%) | 200.55 (±0.2%) | ||

1084872 Middle East | 1656603 (±2.9%) | 33.092 (±1.7%) | 154.08 (±1.0%) | ||

822936 East Europe | 923202 (±0.7%) | 22.513 (±0.8%) | 140.37 (±0.2%) | ||

539416 Africa | 4927450 (±5.2%) | 47.430 (±1.0%) | 249.07 (±1.0%) | ||

180123 South-East Asia | 257682 (±2.8%) | 32.081 (±1.7%) | 153.46 (±1.0%) | ||

119623 East Asia | 109071 (±0.7%) | 10.317 (±3.1%) | 40.85 (±1.6%) | ||

84992 China total | 83447 (±0.2%) | 5.896 (±1.3%) | 37.08 (±0.3%) | ||

10953 Australia - Oceania | 8870 (±0.6%) | 9.029 (±3.0%) | 86.29 (±0.4%) |

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.