[Scilab-users] Fwd: Re: Corona modelling
Stéphane Mottelet
stephane.mottelet at utc.fr
Thu Apr 30 19:12:12 CEST 2020
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 :
> 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 <chinluh.tan at bytecode-asia.com
> <mailto:chinluh.tan at bytecode-asia.com>>
> To: "Users mailing list for Scilab"<users at lists.scilab.org
> <mailto:users at 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://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series
> <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://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv
> <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://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv
> <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://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv
> <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
> <stephane.mottelet at utc.fr <mailto:stephane.mottelet at 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
> http://www.utc.fr/~mottelet <https://antispam.utc.fr/proxy/1/c3RlcGhhbmUubW90dGVsZXRAdXRjLmZy/www.utc.fr/~mottelet>
>
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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
http://www.utc.fr/~mottelet
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