元胞自动机基于元胞自动机模拟HIV传染matlab源码

Posted 2021-06-11 Matlab咨询QQ1575304183

一、模型介绍

元胞自动机就是类似于一个系统，各个单元，即元胞都有联系，但整体的系统又对各个小的单元产生影响，这影响便可定义为规则。而我们的目的就是找到这些规则来进行预测未来系统的发展。(通过计算机的计算，来模拟一个复杂的自然过程)

元胞自动机是一种时间、空间、状态都离散，空间相互作用和时间因果关系为局部的网格动力学模型，具有模拟复杂系统时空演化过程的能力。

元胞自动机(CA)是一种用来仿真局部规则和局部联系的方法。典型的元

胞自动机是定义在网格上的，每一个点上的网格代表一个元胞与一种有限的状

态。变化规则适用于每一个元胞并且同时进行。

组成：

元胞：一个元胞相当于一个储存元件、可以记录状态

元胞空间：分为一维、二维、三维

状态和初始状态：就是初始的状态领域：一个元胞的领域由其周围的元胞组成

转换规则：状态转移的函数(多个规则一起作用)(典型的变化规则，决定于元胞的状态，以及其( 4 或 8 )邻居的状态。)

简单讲解：https://wk.baidu.com/view/ddf76d8c50e2524de4187e45?pcf=2&bfetype=new

二、模型适用范围或案例

1、容易与GIS、遥感数据处理等系统结合

2、城市发展演变、土地利用变化

3、应用于物理模拟，生物模拟等领域

4、森林火灾模拟

三、模型输入变量解释

元胞自动机仿真需要理解三点：

1、元胞：在matlab中可以理解为矩阵中的一点或多点组成的方形块，一般我们用矩阵中的一点代表一个元胞。

2、变化规则：元胞的变化规则决定元胞下一刻的状态。

3、元胞的状态：元胞的状态是自定义的，通常是对立的状态，比如生物的存活状态或死亡状态，红灯或绿灯，该点有障碍物或者没有障碍物等等。

四、模型输出变量解释

输出的是一个演化过程演化结束后的路径

五、模型相应代码

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%% NOTE


% Total runtime for this code was about 7.5 hours on the computer used by

% the original researcher (for 1000 run average of 3 different adherence

% values).

% This runtime is expected to vary from device to device, and is being used

% here to obtain a general comparison for runtime for the different

% models. It is not for a universal runtime value


% When run, this code does not display anything at the beginning. However,

% it will produce 2 graphs for each P_adh value every 2.5 hours approx.


%% Documentation


% n size of each dimension of our square cellular automata grid

% P_HIV fraction (probability) of cells initially infected by virus

% P_i probability of a healthy cell becoming infected if its

% neighborhood contains 1 I1 cell or X I2 cells

% P_v probability of a healthy cell becoming infected by coming

% in contact with a virus randomly (not from its

% neighborhood)

% P_RH Probability of a dead cell becoming replaced by a healthy

% cell

% P_RI Probability of a dead cell becoming replaced by an infected

% cell

% P_T1 Probability of a healthy cell receiving therapy 1

% P_iT1 Probability of a healthy cell receiving therapy 1 of

% becoming infected

% P_T2 Probability of a healthy cell receiving therapy 2

% P_iT2 Probability of a healthy cell receiving therapy 2 of

% becoming infected

% X Number of I2 cells in the neighborhood of an H cell that

% can cause it to become infected

% tau1 tau1 is the number of timesteps it takes for an acute

% infected cell to become latent.

% tau2 tau2 is the number of timesteps it takes for a latent

% infected cell to become dead.

% tau3 tau3 is the number of timesteps after which a healthy

% cell receiving dual therapy becomes a healthy cell again

% totalsteps totalsteps is the total number of steps of the CA (the

% total number of weeks of simulations)

% grid our cellular automata (CA) grid

% tempgrid tempgrid is a temporary grid full of random numbers that is

% used to randomly add different states to our CA grid.

% taugrid taugrid is a grid the same size as our CA grid that stores

% the number of timesteps that a cell has been in state I_1.

% If the number reaches tau1, then the state changes to I_2.

% state state is a [9 x totalsteps] size matrix that stores

% the total number of cells in each state at each timestep

% and the last 2 rows store total healthy and total infected

% cells

% timestep each simulation step of the cellular automata

% 1 timestep = 1 week of time in the real world

% nextgrid nextgrid is a temporary grid. It is a copy of the CA grid

% from the previous simulation. It stores all the CA rule

% updates of the current timestep and stores it all back to

% the grid to display.


%% Clean-up


clc; % clears command window

clear all; % clears workspace and deletes all variables

% close all; % closes all open figures


%% Parameters


n = 100; % meaning that our grid will have the dimensions n x n

P_HIV = 0.05; % initial grid will have P_hiv acute infected cells

P_i = 0.997; % probability of infection by neighbors

P_v = 0.00001; % probability of infection by random viral contact

P_RH = 0.99; % probability of dead cell being replaced by healthy

P_RI = 0.00001; % probability of dead cell being replaced by infected

P_T1 = 0.70; % probability of cell receiving therapy 1

P_iT1 = 0.07; % probability of infection of healthy with therapy 1

P_T2 = 0.50; % probability of cell receiving therapy 2

P_iT2 = 0.05; % probability of infection of healthy with therapy 2

X = 4; % there must be at least X I_2 neighbors to infect cell

tau1 = 4; % time delay for I_1 cell to become I_2 cell

tau2 = 1; % time delay for I_2 cell to become D cell

tau3 = 1; % time delay for H_Tb cell to become H cell

totalsteps = 600; % total number of weeks of simulation to be performed

T_start = 20; % The medication therapy will start on week T_start

totalruns = 1000; % total number of times to run the simulation to get an

% average


%% States


% State 1: H: Healthy (Color- Green)

% State 2: H_T1: Healthy with therapy 1 (Color- Red)

% State 3: H_T2: Healthy with therapy 2 (Color- Red)

% State 4: H_Tb: Healthy with dual therapy (Color- Red)

% State 5: I_1: Active Infected (Color- Cyan)

% State 6: I_2: Latent Infected (Color- Blue)

% State 7: D: Dead (Color- Black)


%% Simulation


for P_adh = 0.5:0.2:0.9


state1 = zeros(totalruns,totalsteps);

state2 = zeros(totalruns,totalsteps);

state3 = zeros(totalruns,totalsteps);

state4 = zeros(totalruns,totalsteps);

state5 = zeros(totalruns,totalsteps);

state6 = zeros(totalruns,totalsteps);

state7 = zeros(totalruns,totalsteps);


for run = 1:totalruns



state1dev = std(state1);

state2dev = std(state2);

state3dev = std(state3);

state4dev = std(state4);

state5dev = std(state5);

state6dev = std(state6);

state7dev = std(state7);

state8dev = state1dev + state2dev + state3dev + state4dev;

state9dev = state5dev + state6dev;


state1mean = mean(state1);

state2mean = mean(state2);

state3mean = mean(state3);

state4mean = mean(state4);

state5mean = mean(state5);

state6mean = mean(state6);

state7mean = mean(state7);

state8mean = state1mean + state2mean + state3mean + state4mean;

state9mean = state5mean + state6mean;


% The following lines of code are to display a graph of each state of

% cells during simulation

set(figure, 'OuterPosition', [200 100 700 500]) % sets figure window size

plot( 1:totalsteps , state1mean, 'g', ...

1:totalsteps , state2mean, 'r', ...

1:totalsteps , state3mean, 'm', ...

1:totalsteps , state4mean, 'y', ...

1:totalsteps , state5mean, 'c', ...

1:totalsteps , state6mean, 'b', ...

1:totalsteps , state7mean, 'k' , 'linewidth', 2 );

xlim([0 600]);

ylim([0 10000]);

hold on;

gridxy(20,'Color',[0.8 0.5 0.0],'linewidth',5) ;

errorbar( 1:15:totalsteps , state1mean(1:15:totalsteps), state1dev(1:15:totalsteps), 'g');

errorbar( 1:15:totalsteps , state2mean(1:15:totalsteps), state2dev(1:15:totalsteps), 'r');

errorbar( 1:15:totalsteps , state3mean(1:15:totalsteps), state3dev(1:15:totalsteps), 'm');

errorbar( 1:15:totalsteps , state4mean(1:15:totalsteps), state4dev(1:15:totalsteps), 'y');

errorbar( 1:15:totalsteps , state5mean(1:15:totalsteps), state5dev(1:15:totalsteps), 'c');

errorbar( 1:15:totalsteps , state6mean(1:15:totalsteps), state6dev(1:15:totalsteps), 'b');

errorbar( 1:15:totalsteps , state7mean(1:15:totalsteps), state7dev(1:15:totalsteps), 'k');

legend( 'Therapy start week', 'Healthy', 'Healthy with Therapy1', 'Healthy with Therapy2', ...

'Healthy with dual Therapy', 'Acute Infected', 'Latent Infected', ...

'Dead', 'Location' ,'NorthEast' );

saveas(gcf,strcat('Model3withAdherencePadh',num2str(P_adh*100),'Graph1.pdf'));



% The following lines of code are to display a graph of each state of

% cells during simulation

set(figure, 'OuterPosition', [200 100 700 500]) % sets figure window size

plot( 1:totalsteps , state8mean, 'g', ...

1:totalsteps , state9mean, 'b', ...

1:totalsteps , state7mean, 'k' , 'linewidth', 2 );

xlim([0 600]);

ylim([0 10000]);

hold on;

gridxy(20,'Color',[0.8 0.5 0.0],'linewidth',5) ;

errorbar( 1:15:totalsteps , state8mean(1:15:totalsteps), state8dev(1:15:totalsteps), 'g');

errorbar( 1:15:totalsteps , state9mean(1:15:totalsteps), state9dev(1:15:totalsteps), 'b');

errorbar( 1:15:totalsteps , state7mean(1:15:totalsteps), state7dev(1:15:totalsteps), 'k');

legend( 'Therapy start week', 'Healthy', 'Infected', 'Dead', ...

'Location' ,'NorthEast' );

saveas(gcf,strcat('Model3withAdherencePadh',num2str(P_adh*100),'Graph2.pdf'));


end

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