As there have already been some cases where persons have been infected twice,
you would have to take into account that only a finite percentage of
infected people become immune and that this immunity might also only last
some finite time.
Another thought experiment would be to randomly test some
Antoine,
Thank you for testing. I have filed bug 16397:
https://bugzilla.scilab.org/show_bug.cgi?id=16397
Regards,
Federico Miyara
On 30/03/2020 09:05, Antoine Monmayrant wrote:
Hello Frederico,
I can confirm this (6.0.2 vs 6.1) on linux Ubuntu 18.04 64bits:
6.0.2 -> ~1s
6.1.0 ->
> On 30.03.2020, at 20:37, Tim Wescott wrote:
>
> Someone was tagging "R" as "removed", which works if it's the aggregate
> of "live and no longer contagious" and "dead".
>
> Actually assessing the proportion of R depends on the local health
> system, and, to some extent, the size of the peak
Someone was tagging "R" as "removed", which works if it's the aggregate
of "live and no longer contagious" and "dead".
Actually assessing the proportion of R depends on the local health
system, and, to some extent, the size of the peak -- the main reason
we're quarantining is to bring the peak
R = recovered = people who can not infect others anymore...this includes
the dead people... (or not?)
there are some nice introducton videos at YouTube about thiseven
showing the mentioned model...
numberphile: https://www.youtube.com/watch?v=k6nLfCbAzgo
3brown1blue:
It is generally assumed that 1% of the infected will die. But that would not be
part of the modelling, depends mainly on local health services.
Heinz
> On 30.03.2020, at 16:12, Vesela Pasheva wrote:
>
> Hello colleagues,
>
> I would like to know whether the variable D of dead persons could
Hi,
In Wikipedia article " Mathematical modelling of infectious disease " it says
that people counted in R have been infected and removed from the disease due to
immunization or death.
I am not an expert but the fatality ratio could be defined as a percentage of
the people infected 'I'.
Hello colleagues,
I would like to know whether the variable D of dead persons could be
included in the model considered. Up till now the model considers the
variables S - susceptible, I - infected and R - recovered. Where do the
Dead persons D go.
Of course i such case the system will be of
> On 30.03.2020, at 08:13, Stéphane Mottelet wrote:
>
> Hello Heinz,
>
> Here is an interactive version (made for my children last week...) :
>
> // Confinement COVID-19 !
> // Stephane MOTTELET, UTC
> // Tue Mar 24 08:55:03 CET 2020
Great many thanks:
o The SIR model is great and
Hi again,
Just tested using the cli (no window, no java): it's even more : 0.34s vs 46s.
Antoine
Le Lundi, Mars 30, 2020 11:56 CEST, Federico Miyara
a écrit:
>
> Dear All,
>
> I have observed that Scilab 6.1 seems to have a regression respect to
> 6.0.2. Sometimes one forgets to put
Hello Frederico,
I can confirm this (6.0.2 vs 6.1) on linux Ubuntu 18.04 64bits:
6.0.2 -> ~1s
6.1.0 -> ~50s
Could you fill a bug report?
Antoine
Le Lundi, Mars 30, 2020 11:56 CEST, Federico Miyara
a écrit:
>
> Dear All,
>
> I have observed that Scilab 6.1 seems to have a
Dear All,
I have observed that Scilab 6.1 seems to have a regression respect to
6.0.2. Sometimes one forgets to put semicolon after the coputation of a
vector with tens of thousands components. Scilab 6.0.2 listed all the
components very fast. That was nice because one hadn't to cancel the
Merci Stéphane, for the very interesting code and Heinz for the reference to
the math behind the epidemy “curve”, or one of its models.
From: users On Behalf Of Stéphane Mottelet
Sent: Monday, March 30, 2020 9:14 AM
To: users@lists.scilab.org
Subject: Re: [Scilab-users] Corona modelling
Hello Heinz,
Here is an interactive version (made for my children last week...) :
// Confinement COVID-19 !
// Stephane MOTTELET, UTC
// Tue Mar 24 08:55:03 CET 2020
//
function dydt=sir(t, y, bet, gam, N)
dydt=[-bet/N*y(1)*y(2)
bet/N*y(1)*y(2)-gam*y(2)
gam*y(2)];
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