seir模型matlab代码

Sure! Here's an example of a MATLAB code implementation for the SEIR model:

% SEIR Model Parameters
N = 1000;       % Total population size
beta = 0.3;     % Contact rate
gamma = 0.1;    % Recovery rate
sigma = 0.2;    % Incubation rate

% Initial conditions
I0 = 1;         % Initial number of infected individuals
E0 = 0;         % Initial number of exposed individuals
R0 = 0;         % Initial number of recovered individuals
S0 = N - I0 - E0 - R0;   % Initial number of susceptible individuals

% Time vector
tStart = 0;
tEnd = 100;
tStep = 1;
tspan = tStart:tStep:tEnd;

% SEIR Model equations
dSdt = @(t, S, E, I, R) -beta * S * I / N;
dEdt = @(t, S, E, I, R) beta * S * I / N - sigma * E;
dIdt = @(t, S, E, I, R) sigma * E - gamma * I;
dRdt = @(t, S, E, I, R) gamma * I;

% Solve the SEIR equations
[~, SEIR] = ode45(@(t, y) [dSdt(t, y(1), y(2), y(3), y(4));
                           dEdt(t, y(1), y(2), y(3), y(4));
                           dIdt(t, y(1), y(2), y(3), y(4));
                           dRdt(t, y(1), y(2), y(3), y(4))], tspan, [S0, E0, I0, R0]);

% Plot the results
plot(tspan, SEIR(:, 1), 'b', 'LineWidth', 2); hold on;
plot(tspan, SEIR(:, 2), 'r', 'LineWidth', 2);
plot(tspan, SEIR(:, 3), 'g', 'LineWidth', 2);
plot(tspan, SEIR(:, 4), 'm', 'LineWidth', 2);
legend('Susceptible', 'Exposed', 'Infected', 'Recovered');
xlabel('Time');
ylabel('Population');
title('SEIR Model Simulation');

This code uses the `ode45` function to solve the differential equations of the SEIR model. The results are then plotted to visualize the dynamics of the susceptible, exposed, infected, and recovered populations over time.

Remember to adjust the parameters and initial conditions according to your specific scenario.