MATLAB Code for Infectious Disease Spread Simulation Based on Cellular Automata

Cellular automata represent a discrete spatial and temporal modeling framework composed of numerous simple units (cells) that evolve according to local interaction rules. In infectious disease spread research, this model effectively visualizes virus transmission processes within populations.

The fundamental approach partitions populations into grid-based cells where each cell's state can be Susceptible (S), Infected (I), or Recovered (R), implementing the classic SIR epidemic model. During algorithmic iterations, infected cells probabilistically influence adjacent susceptible cells, while infections may recover after specific durations. By adjusting parameters like transmission probability and recovery周期, the model simulates diseases with varying transmission intensities.

The MATLAB implementation typically utilizes matrices for storing cell states with iterative state updates through loops. To enhance computational efficiency, vectorized matrix operations can replace individual cell processing. Boundary conditions (periodic or fixed) significantly impact transmission patterns and should be selected based on practical requirements. Final results can be visualized through dynamic heatmaps or statistical curves to observe transmission trends and peak timing effectively.

This model serves not only theoretical analysis but also integrates real-world data (population density, mobility patterns) for parameter optimization, providing valuable references for epidemic control strategies.