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build(matlab): add simulations for pernambuco
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Add Matlab files, adapter for the Pernambuco scenario.

It is important to let it be noted that the simulations for Pernambuco
are in inital stage and incomplete. In order to properly model the
COVID-19 evolution in the state of Pernambuco, model parameters should
be updated.

Fitting model parameters for such a complex model is not trivial and
therefore, it was decided that this should be a future work.
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lcbm committed Nov 27, 2021
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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -73,7 +73,7 @@ This folder contains all of the collected data that will be used as input the **

### `/matlab`

This folder contains all the Matlab SIDARTHE simulation files from both the original paper as well as the adapted by us.
This folder contains all the Matlab SIDARTHE simulation files from both the original paper as well as the adapted by us. The adapted Matlab files end with `_pernambuco.m`. The name `pernambuco` refers to the Brazilian state to which the simulation was adapter for. If you are contributing to this project or want to simply input your data, you will most likely make changes to files in this folder.

### `/themis`

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389 changes: 389 additions & 0 deletions matlab/Sidarthe_SensitivityAnalysis_pernambuco.m
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% MATLAB Code for epidemic simulations with the SIDARTHE model in the work
% SENSITIVITY ANALYSIS: See the effect of changing the parameters
%
% Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy
% by Giulia Giordano, Franco Blanchini, Raffaele Bruno, Patrizio Colaneri, Alessandro Di Filippo, Angela Di Matteo, Marta Colaneri
%
% Giulia Giordano, April 5, 2020
% Contact: [email protected]
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

clear all
close all
clc

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% DATA
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Pernambuco population
popolazione=9e5;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% PARAMETERS
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Simulation horizon: CAN BE MODIFIED AT ONE'S WILL
Orizzonte = 350;

% Plot yes/no: SET TO 1 IF PDF FIGURES MUST BE GENERATED, 0 OTHERWISE
plotPDF = 0;

% Time-step for Euler discretisation of the continuous-time system
step=0.01;

% Transmission rate due to contacts with UNDETECTED asymptomatic infected
alfa=0.57;
% Transmission rate due to contacts with DETECTED asymptomatic infected
beta=0.0114;
% Transmission rate due to contacts with UNDETECTED symptomatic infected
gamma=0.456;
% Transmission rate due to contacts with DETECTED symptomatic infected
delta=0.0114;

% Detection rate for ASYMPTOMATIC
epsilon=0.171;
% Detection rate for SYMPTOMATIC
theta=0.3705;

% Worsening rate: UNDETECTED asymptomatic infected becomes symptomatic
zeta=0.1254;
% Worsening rate: DETECTED asymptomatic infected becomes symptomatic
eta=0.1254;

% Worsening rate: UNDETECTED symptomatic infected develop life-threatening
% symptoms
mu=0.0171;
% Worsening rate: DETECTED symptomatic infected develop life-threatening
% symptoms
nu=0.0274;

% Mortality rate for infected with life-threatening symptoms
tau=0.01;

% Recovery rate for undetected asymptomatic infected
lambda=0.0342;
% Recovery rate for detected asymptomatic infected
rho=0.0342;
% Recovery rate for undetected symptomatic infected
kappa=0.0171;
% Recovery rate for detected symptomatic infected
xi=0.0171;
% Recovery rate for life-threatened symptomatic infected
sigma=0.0171;

% Rate of loss of immunity
ki=0;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% DEFINITIONS
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Parameters
r1=epsilon+zeta+lambda;
r2=eta+rho;
r3=theta+mu+kappa;
r4=nu+xi;
r5=sigma+tau;

% Initial R0
R0_iniziale=alfa/r1+beta*epsilon/(r1*r2)+gamma*zeta/(r1*r3)+delta*eta*epsilon/(r1*r2*r4)+delta*zeta*theta/(r1*r3*r4)

% Time horizon
t=1:step:Orizzonte;

% Vectors for time evolution of variables
S=zeros(1,length(t));
I=zeros(1,length(t));
D=zeros(1,length(t));
A=zeros(1,length(t));
R=zeros(1,length(t));
T=zeros(1,length(t));
H=zeros(1,length(t));
H_diagnosticati=zeros(1,length(t)); % DIAGNOSED recovered only!
E=zeros(1,length(t));

% Vectors for time evolution of actual/perceived Case Fatality Rate
M=zeros(1,length(t));
P=zeros(1,length(t));

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% INITIAL CONDITIONS
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

I(1)=200/popolazione;
D(1)=20/popolazione;
A(1)=1/popolazione;
R(1)=2/popolazione;
T(1)=0.00;
H(1)=0.00;
E(1)=0.00;
S(1)=1-I(1)-D(1)-A(1)-R(1)-T(1)-H(1)-E(1);

H_diagnosticati(1) = 0.00; % DIAGNOSED recovered only
Infetti_reali(1)=I(1)+D(1)+A(1)+R(1)+T(1); % Actual currently infected

M(1)=0;
P(1)=0;

% Whole state vector
x=[S(1);I(1);D(1);A(1);R(1);T(1);H(1);E(1);H_diagnosticati(1);Infetti_reali(1)];

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SIMULATION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% "Control" binary variables to compute the new R0 every time a policy has
% changed the parameters
plottato = 0;
plottato1 = 0;
plottato_bis = 0;
plottato_tris = 0;
plottato_quat = 0;
plottato_fin = 0;

for i=2:length(t)

if (i>4/step) % Basic social distancing (awareness, schools closed)
alfa=0.4218;
gamma=0.285;
beta = 0.0057;
delta=0.0057;
if plottato == 0 % Compute the new R0
r1=epsilon+zeta+lambda;
r2=eta+rho;
r3=theta+mu+kappa;
r4=nu+xi;
r5=sigma+tau;
R0_primemisure=alfa/r1+beta*epsilon/(r1*r2)+gamma*zeta/(r1*r3)+delta*eta*epsilon/(r1*r2*r4)+delta*zeta*theta/(r1*r3*r4)
plottato = 1;
end
end

if (i>12/step)
% Screening limited to / focused on symptomatic subjects
epsilon=0.1425;
if plottato1 == 0
r1=epsilon+zeta+lambda;
r2=eta+rho;
r3=theta+mu+kappa;
r4=nu+xi;
r5=sigma+tau;
R0_primemisureeps=alfa/r1+beta*epsilon/(r1*r2)+gamma*zeta/(r1*r3)+delta*eta*epsilon/(r1*r2*r4)+delta*zeta*theta/(r1*r3*r4)
plottato1 = 1;
end
end

if (i>22/step) % Social distancing: lockdown, mild effect

alfa=0.36;
beta=0.005;
gamma=0.2;
delta=0.005;

mu = 0.008;
nu = 0.015;

zeta=0.034;
eta=0.034;

lambda=0.08;
rho=0.0171;
kappa=0.0171;
xi=0.0171;
sigma=0.0171;

if plottato_bis == 0 % Compute the new R0
r1=epsilon+zeta+lambda;
r2=eta+rho;
r3=theta+mu+kappa;
r4=nu+xi;
r5=sigma+tau;
R0_secondemisure=(alfa*r2*r3*r4+epsilon*beta*r3*r4+gamma*zeta*r2*r4+delta*eta*epsilon*r3+delta*zeta*theta*r2)/(r1*r2*r3*r4)
plottato_bis = 1;
end
end

if (i>28/step) % Social distancing: lockdown, strong effect

alfa=0.21;
gamma=0.11;

if plottato_tris == 0 % Compute the new R0
r1=epsilon+zeta+lambda;
r2=eta+rho;
r3=theta+mu+kappa;
r4=nu+xi;
r5=sigma+tau;
R0_terzemisure=(alfa*r2*r3*r4+epsilon*beta*r3*r4+gamma*zeta*r2*r4+delta*eta*epsilon*r3+delta*zeta*theta*r2)/(r1*r2*r3*r4)
plottato_tris = 1;
end
end


if (i>38/step) % Broader diagnosis campaign

epsilon = 0.2;
rho=0.02;
kappa=0.02;
xi=0.02;
sigma=0.01;

zeta=0.025;
eta=0.025;

if plottato_quat == 0 % Compute the new R0
r1=epsilon+zeta+lambda;
r2=eta+rho;
r3=theta+mu+kappa;
r4=nu+xi;
r5=sigma+tau;
R0_quartemisure=(alfa*r2*r3*r4+epsilon*beta*r3*r4+gamma*zeta*r2*r4+delta*eta*epsilon*r3+delta*zeta*theta*r2)/(r1*r2*r3*r4)
plottato_quat = 1;
end
end

% OUTLOOK: to EXPLORE POSSIBLE SCENARIOS,
% change the parameters (after 50 days)
if (i>50/step)

% CHANGE THE PARAMETERS HERE:
% parameter=nominalvalue*scalingfactor;
% Set the scaling factor to some value different from 1
% for one or more parameters
% to explore the effect of different parameter values

alfa=0.2100*1;
beta=0.0050*1;
gamma=0.1100*1;
delta=0.0050*1;

epsilon= 0.2000*1;
theta = 0.3705*1;

zeta = 0.0250*1;
eta = 0.0250*1;

mu = 0.008*1;
nu = 0.0150*1;

tau = 0.0100*1;

lambda = 0.0800*1;
rho = 0.0200*1;
kappa = 0.0200*1;
xi = 0.0200*1;
sigma = 0.0100*1;

ki=0;

if plottato_fin == 0 % Compute the new R0
r1=epsilon+zeta+lambda;
r2=eta+rho;
r3=theta+mu+kappa;
r4=nu+xi;
r5=sigma+tau;
R0_final=(alfa*r2*r3*r4+epsilon*beta*r3*r4+gamma*zeta*r2*r4+delta*eta*epsilon*r3+delta*zeta*theta*r2)/(r1*r2*r3*r4)
plottato_fin = 1;
end

end

% Compute the system evolution

B=[-alfa*x(2)-beta*x(3)-gamma*x(4)-delta*x(5) 0 0 0 0 0 ki 0 0 0;
alfa*x(2)+beta*x(3)+gamma*x(4)+delta*x(5) -(epsilon+zeta+lambda) 0 0 0 0 0 0 0 0;
0 epsilon -(eta+rho) 0 0 0 0 0 0 0;
0 zeta 0 -(theta+mu+kappa) 0 0 0 0 0 0;
0 0 eta theta -(nu+xi) 0 0 0 0 0;
0 0 0 mu nu -(sigma+tau) 0 0 0 0;
0 lambda rho kappa xi sigma -ki 0 0 0;
0 0 0 0 0 tau 0 0 0 0;
0 0 rho 0 xi sigma 0 0 0 0;
alfa*x(2)+beta*x(3)+gamma*x(4)+delta*x(5) 0 0 0 0 0 0 0 0 0];
x=x+B*x*step;

% Update variables

S(i)=x(1);
I(i)=x(2);
D(i)=x(3);
A(i)=x(4);
R(i)=x(5);
T(i)=x(6);
H(i)=x(7);
E(i)=x(8);

H_diagnosticati(i)=x(9);
Infetti_reali(i)=x(10);

% Update Case Fatality Rate

M(i)=E(i)/(S(1)-S(i));
P(i)=E(i)/((epsilon*r3+(theta+mu)*zeta)*(I(1)+S(1)-I(i)-S(i))/(r1*r3)+(theta+mu)*(A(1)-A(i))/r3);

end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% FINAL VALUES
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Variables
Sbar=S(length(t));
Ibar=I(length(t));
Dbar=D(length(t));
Abar=A(length(t));
Rbar=R(length(t));
Tbar=T(length(t));
Hbar=H(length(t));
Ebar=E(length(t));

% Case fatality rate
Mbar=M(length(t));
Pbar=P(length(t));

% Case fatality rate from formulas
Mbar1=Ebar/(S(1)-Sbar);
Pbar1=Ebar/((epsilon*r3+(theta+mu)*zeta)*(I(1)+S(1)-Sbar-Ibar)/(r1*r3)+(theta+mu)*(A(1)-Abar)/r3);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% FIGURES
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

figure
plot(t,Infetti_reali,'b',t,I+D+A+R+T,'r',t,H,'g',t,E,'k')
hold on
plot(t,D+R+T+E+H_diagnosticati,'--b',t,D+R+T,'--r',t,H_diagnosticati,'--g')
xlim([t(1) t(end)])
%title('Actual vs. Diagnosed Epidemic Evolution')
xlabel('Time (days)')
ylabel('Cases (fraction of the population)')
legend({'Cumulative Infected','Current Total Infected', 'Recovered', 'Deaths','Diagnosed Cumulative Infected','Diagnosed Current Total Infected', 'Diagnosed Recovered'},'Location','east')
grid

if plotPDF==1
set(gcf, 'PaperUnits', 'centimeters');
set(gcf, 'PaperPosition', [0 0 24 16]);
set(gcf, 'PaperSize', [24 16]); % dimension on x axis and y axis resp.
print(gcf,'-dpdf', ['PanoramicaEpidemiaRealevsPercepita.pdf'])
end
%

figure
plot(t,I,'b',t,D,'c',t,A,'g',t,R,'m',t,T,'r')
xlim([t(1) t(end)])
%title('Infected, different stages, Diagnosed vs. Non Diagnosed')
xlabel('Time (days)')
ylabel('Cases (fraction of the population)')
legend({'Infected ND AS', 'Infected D AS', 'Infected ND S', 'Infected D S', 'Infected D IC'},'Location','northeast')
grid

if plotPDF==1
set(gcf, 'PaperUnits', 'centimeters');
set(gcf, 'PaperPosition', [0 0 24 16]);
set(gcf, 'PaperSize', [24 16]); % dimension on x axis and y axis resp.
print(gcf,'-dpdf', ['SuddivisioneInfetti.pdf'])
end
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