World wide without data from China, we are currently seeing a purely exponential growth with a doubling time of 4 days. We are in a process of fast exponential growth and will see large numbers for the next weeks. A typical phenomenon of exponential growth.

The numbers outside China have surpassed the numbers of China by several factors.

Any hospital system will be overrun by exponential growth, even when we are slowing down the growth, because when 15% have severe conditions and require treatment for 2-3 weeks, capacities quickly fill up. A sufficient slow down will take several years, which is not practical. A solution would be to implement heaving testing options as artifical herd immunity. This way we can set the symptomless carriers in quarantine and stop the growth process. China and South Korea have show this method to be successful.

Three data sections are available: Worldwide, China only, and Worldwide without China. Also available are the fits for each individual region or country or continental areas. An advanced Gompertz function has been implemented. The old data are available with the information „simple Gompertz“.

Applied maths is explained here.

Fits and +10 days predictions for each individual region or country.

Fits and +10 days predictions by continental regions.

Fits and +10 days predictions on death rates.

Compare with Ebola data 2014/2015

Simple Gompertz: Fits and +10 days predictions for each individual region or country.

Simple Gompertz: Fits and +10 days predictions by continental regions.

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The classical way to calculate the death rate, taking the data of the actual day. However, there is a delay between diagnosis and death. Therefore the death should be counted to the day of diagnosis, which is barely done. In the graph above, we seem from the turning point an average delay between diagnosis and death of about 6 days.

What you see below is a rise at the end. It is caused by the delay of the death curve of about 6 days: So when the blue curve above rises quickly, the read death curve lacks behind causing lower calculated death rates. Once the blue curve fades out asymptoticially, the rise is over and now the second rise of the red death curve follows that is visible below. After all the process has passed, the final death rate can be calculated. However, if we fit the data and have passed the turning point of the red curve, the maximum numbers become more certain and provide a better prediction.

We can read from the data outside China, that they are growing
on an expected exponential curve. No sign of any control,
otherwise we would see a turning point approaching and the curve
to tip over to the right. The T_{2} appears here much
bigger, as we are far away from the turning point, so
N_{max} has a huge error. Fitted with the logistic
function, we see a doubling time of about 4 days, like in the
data in China. This is extremely fast and leaves little time for
reaction. Most governments have started to react now and setup
restrictions. Once the restriction is in place, we will see the
data increasing for the next 6 days as if there were no
restrictions. The reason is the delay of the latent infected
people. The average incubation time is about 5-6 days. So when
we stop all contacts, all those within the incubation time will
pop up in the data by that delay. Therefore we need patience and
wait effects to show up, when a restriction was set in place.
(22.03.2020)

.·. Impressum