[Scilab-users] Corona modelling

[hdf]

Hello Heinz,
Before comparing with John Hopkins' data and try to identify the different
parameters I wanted to 'improve' the model. I have read several interesting
articles on the web.  One of them
<http://www.madore.org/~david/weblog/d.2020-04-02.2648.html#d.2020-04-02.2648> 
, written by David Madore, explains (in french) that the recovery model
would be better at a "constant recovery time" instead of using an
"exponential distribution of probability whose expected value is 1/γ" like
in the traditional SIR model. 
I agree with him because I could recognized a "simple time delay" in his
explanations 
So in the below diagram I replaced the gamma (γ) feedback on the infected
integrator by a variable delay and add a super function diagram  to model
the healthcare system with:
 - One input: infected people per day.
 - Two outputs: Recovered and unfortunately dead people per day.
The other parameters are percentages of 'symtomatic', 'hospitabized',
'Reanimated' and 'dead' people on one hand and delays before being 'healed',
'hopitalized' and 'reanimated', but also 'incubation' time on the other
hand.
<http://mailinglists.scilab.org/file/t498052/SEIRM_SystSant%C3%A9_01.png> 
Here are some of my results:
<http://mailinglists.scilab.org/file/t498052/Figure_n%C2%B020023_SEIRM_SystSante_01.png> 
Funny 'infected' curve and its derive right ?
I am thinking of studying the FFT of the real John Hopkings' curves to
identify the delays to put inside my healthcare system model.
But I'm running out of time before I have to work back next monday.
Hervé de Foucault




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