Try this, follow the instructions:
IDo some modification in the equations, I mean replace the equations in the code with yours and parameters as well.
https://www.mathworks.com/matlabcentral/fileexchange/132907-analytical-analysis-of-an-ocp-with-system-of-odes-in-matlab?s_tid=prof_contriblnk
SEIR model implemented in MATLAB:
```matlab
% SEIR Model for Infectious Diseases
% Parameters:
% beta: Transmission rate
% gamma: Recovery rate
% sigma: Incubation rate
% N: Total population
% I0: Initial number of infected individuals
% E0: Initial number of exposed individuals
% R0: Initial number of recovered individuals
% tspan: Time span of the simulation
function seirModel()
% Parameters
beta = 0.2;
gamma = 0.1;
sigma = 0.05;
N = 1000;
I0 = 10;
E0 = 5;
R0 = 2;
tspan = 0:0.1:50;
% Solve the differential equations
[t, y] = ode45(@seirEquations, tspan, [N-I0-E0-R0, E0, I0, R0]);
% Plot the results
plot(t, y(:, 1), 'b', 'LineWidth', 2); hold on;
plot(t, y(:, 2), 'r', 'LineWidth', 2);
plot(t, y(:, 3), 'g', 'LineWidth', 2);
plot(t, y(:, 4), 'k', 'LineWidth', 2);
grid on;
xlabel('Time');
ylabel('Population');
legend('Susceptible', 'Exposed', 'Infected', 'Recovered');
title('SEIR Model');
end
function dydt = seirEquations(t, y)
% Variables
S = y(1);
E = y(2);
I = y(3);
R = y(4);
% Differential equations
dSdt = -beta * S * I / N;
dEdt = beta * S * I / N - sigma * E;
dIdt = sigma * E - gamma * I;
dRdt = gamma * I;
dydt = [dSdt; dEdt; dIdt; dRdt];
end
```
To run the code, simply call the `seirModel()` function. You can modify the parameter values (`beta`, `gamma`, `sigma`, `N`, `I0`, `E0`, `R0`, `tspan`) to customize the simulation according to your requirements.
Note: The code uses the `ode45` function in MATLAB's Ordinary Differential Equation (ODE) solver to solve the SEIR equations numerically. Make sure you have the MATLAB ODE toolbox installed.
SEIR model implemented in Mathematica:
```mathematica
(* SEIR Model for Infectious Diseases *)
(* Parameters *)
beta = 0.2; (* Transmission rate *)
gamma = 0.1; (* Recovery rate *)
sigma = 0.05; (* Incubation rate *)
N = 1000; (* Total population *)
I0 = 10; (* Initial number of infected individuals *)
E0 = 5; (* Initial number of exposed individuals *)
R0 = 2; (* Initial number of recovered individuals *)
tmax = 50; (* Maximum time *)
(* Solve the differential equations *)
sol = NDSolve[{S'[t] == -beta*S[t]*I[t]/N,
E'[t] == beta*S[t]*I[t]/N - sigma*E[t],
I'[t] == sigma*E[t] - gamma*I[t],
R'[t] == gamma*I[t], S[0] == N - I0 - E0 - R0,
E[0] == E0, I[0] == I0, R[0] == R0}, {S, E, I, R}, {t, 0, tmax}];
(* Plot the results *)
Plot[Evaluate[{S[t], E[t], I[t], R[t]} /. sol], {t, 0, tmax},
PlotLegends -> {"Susceptible", "Exposed", "Infected", "Recovered"},
PlotStyle -> {Blue, Red, Green, Black}, Frame -> True,
FrameLabel -> {"Time", "Population"},
PlotLabel -> "SEIR Model", GridLines -> Automatic]
```
To run the code, simply evaluate the entire Mathematica code block. You can modify the parameter values (`beta`, `gamma`, `sigma`, `N`, `I0`, `E0`, `R0`, `tmax`) to customize the simulation as per your requirements.
The code uses `NDSolve` to numerically solve the SEIR equations. The results are then plotted using `Plot`, displaying the population dynamics over time.
Good luck
hello sir, what about this one
https://github.com/mathworks/modeling-disease-spread-using-an-SEIR-model
the code is in github and is open source
so, you can esaily modife it according to your own needs. i also have some books about mathlab that could be interesting for you
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