传统传染病建模的matlab程序

function [pCount_X pCount_Y pCount_Z] = discreteSIR(N,beta,gamma,T,K) % discrete-time, discrete-space, run 200times % if a individual is a susceptible, the value is 0, infected 1, recovery 2; rand('state',sum(100*clock)); %random seed %Initialize for k = 1 : K Population_List = zeros(1,N); P_Zero = randperm(100);%random 100 infected individuals for i = 1 : size(P_Zero,2) Population_List(P_Zero(i)) = 1; end Count_X{k} = []; % calculate the susceptible individuals in each time step; Count_Y{k} = []; % calculate the infected individuals in each time step; Count_Z{k} = []; % calculate the recovery individuals in each time step; pCount_X{k} = []; %probability of X; pCount_Y{k} = []; %probability of Y; pCount_Z{k} = []; %probability of Z; new_Y{k} = [];%the newly cases in each time step new_Y{k}(1,1) = 0; new_R{k} = [];%the newly recovery in each time step new_R{k}(1,1) = 0; Count_Y{k}(1,1) = sum(Population_List == 1); Count_X{k}(1,1) = N - Count_Y{k}(1,1); while sum(Population_List == 1) > 0 sumX = 0; sumY = 0; temp_X = []; temp_X = find(Population_List == 0); for i = 1 : size(temp_X,2) if rand < 0.4 && (Population_List(1 + floor(rand() * N)) == 1) Population_List(temp_X(i)) = 1; sumX = sumX + 1; end end new_Y{k} = [new_Y{k}, sumX]; temp_Y = []; temp_Y = find(Population_List == 1); for j = 1 : size(temp_Y,2) if rand < gamma Population_List(temp_Y(j)) = 2; sumY = sumY + 1; end end new_R{k} = [new_R{k}, sumY]; end %calculate the number of Susceptible, Infected and recovery for i = 2 : T Count_X{k}(i) = Count_X{k}(i-1) - new_Y{k}(i); Count_Y{k}(i) = Count_Y{k}(i-1) + new_Y{k}(i)- new_R{k}(i); end Count_Z{k} = N - Count_X{k} - Count_Y{k}; pCount_X{k} = Count_X{k} / N; pCount_Y{k} = Count_Y{k} / N; pCount_Z{k} = Count_Z{k} / N; end %figure %for k = 1 : 200 % plot(pCount_X{k},'ok','MarkerSize',3); % hold on % plot(pCount_Y{k},'ok','MarkerSize',3); %hold on %plot(pCount_Z{k},'ok','MarkerSize',3); %end