Deterministic infectious disease models are simple in form and convenient for mathematical processing, making them suitable for macro trend analysis and theoretical derivation. However, they ignore the random fluctuations of individual behaviors and the environment, making it difficult to depict discrete scenarios such as small groups and the initial stages of outbreaks. Random models are closer to the actual transmission process, capable of reflecting random noise, individual differences, and rare events. The results are more relevant to reality, but they have complex structures, are difficult to solve, require high computational costs, and the theoretical analysis is relatively cumbersome. In practical research, these two types of models are often combined to balance the simplicity of theory with the rationality of reality.