Thanks Stephane for your sharing.
I agreed with you that the Initial population (susceptible individuals) is one
of the unknown parameters, so there will be 3 parameters to tune in SIR model.
As the parameters are somehow related to each other, and I read somewhere that
the Gamma is rather fixed as the recovery days shall be somehow known by now.
So if we can set the Gamma to a smaller range, we would have only 2 to adjust
to get some reasonable prediction?
One more parameter is the starting date, as the data started from 1/22/20, but
I think each country shall be adjusted for their actual date where the virus
start to spread.
Any ideas are welcome.
Thanks.
Regards,
Chin Luh
---- On Fri, 01 May 2020 01:12:12 +0800 Stéphane Mottelet
<stephane.motte...@utc.fr> wrote ----
Hi,
My experience with the SIR model (not SEIR) is that the estimated
parameters are very sensitive to the initial condition of the
Infected pool. Hence, this initial condition should be also
considered as a parameter to be identified. However, the main
problem (at least with France data) is that fitting Infected and
Recovered (cured or dead people) together gives not satisfactory
results.
My two cents...
S.
Le 30/04/2020 à 18:44, Chin Luh Tan a
écrit :
--
Stéphane Mottelet
Ingénieur de recherche
EA 4297 Transformations Intégrées de la Matière Renouvelable
Département Génie des Procédés Industriels
Sorbonne Universités - Université de Technologie de Compiègne
CS 60319, 60203 Compiègne cedex
Tel : +33(0)344234688
http://www.utc.fr/~mottelet
_______________________________________________
users mailing list
users@lists.scilab.org
http://lists.scilab.org/mailman/listinfo/users
Just notice that this email was stuck due to the image
attached was too large, and notice the new post by Claus with the
SEIR model from
Matlab, perhaps Scilabers could make the model more
realistic together.
CL
============ Forwarded message ============
From: Chin Luh Tan <mailto:chinluh....@bytecode-asia.com>
To: "Users mailing list for Scilab"<mailto:users@lists.scilab.org>
Date: Thu, 30 Apr 2020 00:03:46 +0800
Subject: Re: [Scilab-users] Corona modelling
============ Forwarded message ============
Hi,
I just modified Stephane's nice GUI to make it able
to load the real world data from internet so that we
could overlapped the data to the SIR model to study
the effect of locked-down, and the meaning of the
coefficients.
From some reading from the internet, the
"Susceptible" population is kind of like difficult
to determined, and some suggested to use "current
cases" as "optimum model" assuming the condition is
recovering. As or the "optional model", the "total
population" is being used.
While "beta" is the transmission coefficient,
"gamma" the recovery factor, can anyone explained
more details, perhaps in layman term, how to relate
these parameters to one country condition, such as
the relationship between gamma with the number of
days (what days is it referring to), beta "actual
meaning" in layman term, and perhaps link with some
"technical term" the newspaper always seen on
papers?
I attached the GUI, which will load 3 sets of data
from Johns Hopkins Github,
(https://antispam.utc.fr/proxy/2/c3RlcGhhbmUubW90dGVsZXRAdXRjLmZy/github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series)
which could be download using following 3 lines
directly from the Scilab:
--> fn =
getURL('https://antispam.utc.fr/proxy/2/c3RlcGhhbmUubW90dGVsZXRAdXRjLmZy/raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv')
--> fn =
getURL('https://antispam.utc.fr/proxy/2/c3RlcGhhbmUubW90dGVsZXRAdXRjLmZy/raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv')
--> fn =
getURL('https://antispam.utc.fr/proxy/2/c3RlcGhhbmUubW90dGVsZXRAdXRjLmZy/raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv')
and run the GUI as attached.
rgds,
CL
---- On Mon, 30 Mar 2020 14:13:40 +0800 Stéphane
Mottelet <mailto:stephane.motte...@utc.fr> 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
//
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)];
endfunction
function draw(bet, gam)
t=0:1:360;
N=6e7;
if exists("gcbo") && is_handle_valid(gcbo)
sb = gcbo;
if sb.tag=="beta"
bet=sb.value;
gam=findobj("gamma").value
else
gam=sb.value;
bet=findobj("beta").value
end
y=ode('stiff',[N-1;1;0],0,t,list(sir,bet,gam,N));
curves = findobj("curves");
curves.children(1).data(:,2)=y(3,:);
curves.children(2).data(:,2)=y(2,:);
curves.children(3).data(:,2)=y(1,:);
else
y=ode('stiff',[N-1;1;0],0,t,list(sir,bet,gam,N));
scf(0)
clf
plot(t,y)
gce().tag="curves";
gce().children.thickness=2;
legend("Susceptible","Infected","Recovered",-1)
sb1 = uicontrol("style","slider",...
"units","normalized",...
"Position", [0.85,0.2,0.05,0.48],...
"BackgroundColor", [1,1,1],...
"Callback_Type",12,...
"sliderstep",[1/1000,1/10],...
"min",0.15,"max",0.3,"value",bet,...
"Callback","draw","tag","beta");
uicontrol("style","text",...
"string","$\beta$",...
"units","normalized",...
"Position", [0.85,0.125,0.05,0.08],...
"BackgroundColor", [1,1,1],...
"HorizontalAlignment","center");
sb1 = uicontrol("style","slider",...
"units","normalized",...
"Position", [0.90,0.2,0.05,0.48],...
"BackgroundColor", [1,1,1],...
"Callback_Type",12,...
"sliderstep",[1/1000,1/10],...
"min",0,"max",1/15,"value",gam,...
"Callback","draw","tag","gamma");
uicontrol("style","text",...
"string","$\gamma$",...
"units","normalized",...
"Position", [0.9,0.125,0.05,0.08],...
"BackgroundColor", [1,1,1],...
"HorizontalAlignment","center");
end
end
clf
draw(0.3,1/15)
Le
30/03/2020 à 02:14, Heinz Nabielek a écrit :
Colleagues:
is there an straightforward Scilab approach for solving the three coupled
nonlinear differential equations of first order given by the Standard Model of
Epidemics?
S= number Susceptible: S'=-aSI
I= number Infected: I'=aSI - bI
R= number Recovered: R'=bI
whereby 'a' is the transmission coefficient, 'b' the recovery factor (after
Reed-Frost 1928).
Initial values for S, I, R are available.
Thank you
Heinz
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--
Stéphane Mottelet
Ingénieur de recherche
EA 4297 Transformations Intégrées de la Matière Renouvelable
Département Génie des Procédés Industriels
Sorbonne Universités - Université de Technologie de Compiègne
CS 60319, 60203 Compiègne cedex
Tel : +33(0)344234688
https://antispam.utc.fr/proxy/1/c3RlcGhhbmUubW90dGVsZXRAdXRjLmZy/www.utc.fr/~mottelet
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