See here my current fits from WHO-data.

Data show a jump to +15,000 cases in one day because CT scans were used as well to find infected people. Therefore data show a bit inconsistency and the thurn of the now 7 data points from this is stronger than expected. We need to wait a few more days to follow that trend. If it continues, we might overestimate the numbers.

Today 20^{th} February, I have modified the fit
routines, in order to fit the new trend of recovered
people. Before I had set their N_{max} number to the
average recovered that are shown in a graph below. This was
necessary because the Gompertz function cannot fit
N_{max} well, when it is too far from the turning point,
resulting in unreasonable numbers. The same applied for the
number of deaths. As a solution, I have used now the second
function in the graph to limit N_{max} to the maximal
number of infected people and fit the fraction of it as
sin(m). As the deaths are near or at the turning point, I
substract their maximum number from the maximum number of
infected as a theoretical maximum number of recovered, of that I
fit the fraction.

As a result, I can first time estimate the death rate from
deaths-N_{max} and infected-N_{max}. Interestingly this matches nearly the same value I guessed from my approach by accounting the delay between diagnosis of infection and death, see link or last section.

Please be aware that this number may not be the final number, which we can only count, when the epidemic has ended, because during the epidemic news methods of treatment will be develop and help more people to survive. I hope to expect this number to be smaller.

Three data sections are availble: World, China only, and World without China.

World

China only

World without China

Death rate as function of average delay between infection diagnosis and death

Compare with Ebola data 2014/2015

If you like to support this work:

We can read from the data outside China, that they are growing on an expected exconential curve. No sign of any control, otherwise we would see a turing poing approaching and the curve to tip over to the right. The T_{2} appears here much bigger, as we are far way from the turing point, so N_{max} has a huge error. Fitted with the logistic function, we see a doubling time of about 6 days, like in the data in China. This is extremely fast and leaves little time for reaction. If we do not act now and start intensive control on airports, this curve will continue and the prediction for at least the next 5-10 days is quite certain. In 10 days we will see around 4000 cases outside China. Don't talk, act now!

Method 1: Using the last value of death/recovered rate.

Method 2: Using the average death/recoverd rate.

Method 2 is less sensitive to changes in daily death rate numbers.

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