Wrapping function/model into a gui for easy interaction: No clue how to start anything
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Hello,
I am stuck with a task I am an absolute newbie at, and that I really don't understand.
We have two SIR-epidemiology models, one of which is posted below. One uses the euler-method for calculating, the other does it normally.
We are supposed to make the models interactive by embedding them into a gui which needs to:
- have editable start-values, with a default start value already entered in case the user doesn't change it (S0 I0 R0, tmax)
- have sliders for manipulating the different parameters (b and k)
- plot the model
- respond to changes in slider-vales (b and k) with updating the model accordingly, then updating the plot
Aaaand... I have a wide range of non-knowledge in this topic. So I have quite literally no idea where to properly start, or do any of that.
I've had a rather shitty week of everything not going to plan, and now I have a deadline on sunday and am at pants-on-head levels of clueless right now. That's why I am a bit frantic about this right now.
Thank you.
Sincerely,
Claudius Appel
%% SIR using Euler
% Time range:
tmax = 500;
% Initial values for S, I and R: %% needs to be editable as
% start-vals
S0 = 1;
I0 = 1.27e-6;
R0 = 0;
% Birth rate and death rate parameters: %% needs to be editable as
% start-vals
b = 1/2;
k = 1/3;
% Predict population dynamics for SIR model with Euler's method:
%% this should then update every x seconds or so, while responding to current input
[S, I, R] = SIRmodel(tmax, S0, I0, R0, b, k)
Plot S, I and R vs time:
plot(0:tmax, S, 'b')
hold on
plot(0:tmax, I, 'r')
plot(0:tmax, R, 'g')
title('SIR Model')
legend('S(t)', 'I(t)', 'R(t)')
xlabel('Time, t')
ylabel('Population')
% xlim([0, 40])
hold off
% This function defines S(t), I(t) and R(t) with the Euler's method.
function [S,I,R] = SIRmodel(tmax, S0, I0, R0, b, k)
S = zeros(1, tmax);
I = zeros(1, tmax);
R = zeros(1, tmax);
S(1) = S0;
I(1) = I0;
R(1) = R0;
T(1) = 0;
Delta_t = 1;
for n = 1:tmax
T(n+1) = T(n) + Delta_t;
S(n+1) = S(n) - b * S(n) * I(n) * Delta_t;
I(n+1) = I(n) + (b * S(n) *I(n) - k * I(n)) * Delta_t;
R(n+1) = R(n) + k * I(n) * Delta_t;
end
end
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