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⛄ 内容介绍
本文通过改进传统的疾病传播数学模型,在分析疫情传播特征的基础上,给出了详细描述疫情传播的数学模型.优化该模型后得到的数值解与实际数据基本吻合.通过数据的模拟,得到了"控制"对流行性疾病传播的重要性,以及具有指导意义的疫情控制措施.
⛄ 部分代码
function X_dot = infectious_ODE(T, X)
% ---------------------------------------------------------------------
% Infectious ODE model: Credit to "Epidemiological parameter review
% and comparative dynamics of influenza, respiratory syncytial virus,
% rhinovirus, human coronavirus, and adenovirus"
% ---------------------------------------------------------------------
% Model States:
% ---------------------------------------------------------------------
% S Number of susceptible individuals
% E Number of exposed (not infectious) individuals
% I1 Number of initially infectious individuals
% I2 Number of infected, non-hospitalized individuals
% H Number of hospitalized individuals
% R Number of recovered individuals
% D Number of dead individuals
% ---------------------------------------------------------------------
global N r beta c;
gamma1 = 0.1961; % per capita rate of progress from exposed to infectious state
gamma2 = 0.1176; % per capita rate of progress through initial infectious state
gamma3 = 0.0286; % per capita rate of progress through hospitalized state
gamma4 = 0.1818; % per capita rate of progress through non-hospitalized infectious state
p1 = 0.138; % Proportion of severe patients
p2 = 0.5; % Death rate of severe patients
[S,E,I1,I2,H,R,D] = deal(X(1),X(2),X(3),X(4),X(5),X(6),X(7));
Sdot = -r*beta/N*S*(I1+I2+c*H);
Edot = r*beta/N*S*(I1+I2+c*H) - gamma1*E;
I1dot = gamma1*E - gamma2*I1;
I2dot = gamma2*(1-p1)*I1 - gamma4*I2;
Hdot = gamma2*p1*I1 - gamma3*H;
Rdot = gamma4*I2 + gamma3*(1-p2)*H;
Ddot = gamma3*p2*H;
X_dot = [Sdot,Edot,I1dot,I2dot,Hdot,Rdot,Ddot]';
end
⛄ 运行结果
⛄ 参考文献
[1]肖海军王玲程明. 基于Matlab的疾病传播研究--SARS疫情的传播预测与控制[J]. 计算机与数字工程, 2005, 033(004):50-52.
