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I would like to deliver a bit of material making possible to make own conclusions about the situation with coronavirus. Something that usually unavailable in a bla-bla of mass media.
This material needs some attention. Also a bit of mathematical culture, I think at most at secondary school level.
The order of the data results from the desire to present the most important charts at the beginning.
Please start from ‘The most interesting part - indexes’.
Next please movve to ‘Processed data. Graphs rather obvious, however rarily displayed.’.
A, so called, information delivered by the mass media, i. e. ‘Source data’, is used by scribblers to brain-wash people.
Everybody is being bombed by messages that a number of total infections/deaths reached ....
Guys relax! This is not important!
And what is?
Most important is that we have encountered with a hysteria mainipulated by the mass media. The big pharma is vitally interested in the hysteria.
Return to 2009 and read about swine flu hysteria and the mass media forcing Ewa Kopacz, a polish Ministry of Health to buy new vaccines prepared in a rush. Mrs Kopacz refused to buy those vaccines and required the big pharma companys to take whole responisbility for the side effects.
Several months later, I read in newspapers that the governments of Germany, France and Great Brittain spent really big money for utilization of dozens of millions vaccines.
The case was spiced up by a german program or article (I think that in the Frankfurter Allgemeiner Zeitung) showing biznes connections between the big pharma and officers of the WHO.
‘historia magistra vitae est’. For all?
The processed data cover a state (current number of infected people) and changes of a state (numbers of new daily infections, recoveries an deaths respectively).
A figure below has two scales: a linear one on the left-hand side, a logarithmic one on the righ-hand.
Since an illness distribution process is exponencial by its nature, during the first phase of the epidemic, when a number of carriers increases rapidly, changes of the trend are clearly visible.
When external situation is stable a logarithmic graph consists of line segments. When the situation changes the last segment stops and next one with a different angle starts.
The changes might be like: introducing sanitary limitations, loosing them, closing schools, closing part of enterprises, other actions enabling more frecuent contact of people.
Comments to the mortality.
The deaths have been omitted in the previous figure on purpose.
The death rate in many countries is below 0.4 %. If there are countries with the death rate above 7 %, it smells. Either their statistics is being falsified or doctors state coronavirus as a death reason not having any basis for it. I do not believe that in a group of countries with similat level of living, death rate varies from 0.4 % up to 20 - 30 times higher values.
I have also encountered information that families members as well as directors of medical units asked to/ insisted on reporting the coronavirus as the reason of the death.
I suspect that simple pneumonia would cause lower payments from the social security. Smart people are in every country!
Smoothed graphs, NORMALIZED TO THIS SAME SIZE indicate after how many days infected person dies (if dies of course).
For Poland it lookes like the deaths occure approximately 4 - 6 days after testing of the infection.
Pay attention to scales on the lef-hand and the right-hand axes!!!
The most interesting part. More processed data.
Crossing of the charts would mean that the number of people getting infected every day will equal the number of recoveries. I skip deaths here as something that is relatively small (about 0.3 % for Poland).
It means also that a maximum number of current carriers has been reached and probably the number of carriers will start to drop.
And what is?
Important are: current number of covid carriers and difference among new infections, new recoveries. Please take into account that usually a death rate is on level of 0.3 - 0.4 %. If it is higher, the question is why. It is necessary to have a closer look to the data and find a rational reason for the difference.
Please ref. to the ‘The most interesting part - indexes’.
CSSE JHU has released the following data:
Red blocks relate to the source information from CSSE of JHU. Other information has to be computed. This is a simplest model of course.
carrierstoday = total_infectedtoday - total_deathstoday - total_recoveredtoday
daily_infectionstoday = total_infectedtoday - total_infectedyesterday
daily_deathstoday = total_deathstoday - total_deathsyesterday
daily_recoveriestoday = total_recoveriestoday - total_recoveriesyesterday
There are at least two approaches to more complex model: