Abstract
Microsimulation model research can simulate a large number of micro individuals with different characteristics, build disease progression models, and evaluate the effects and benefits of risk factor control and early intervention strategies used in disease prevention and control, which could overcome the limitations of traditional epidemiological research, such as high investment and long time-consuming, and provide important evidence support for decision-making. This study introduces the definition and methods of microsimulation model, and articulates the application of three modeling methods including Markov model, decision-tree model and discrete event model in the primary and secondary cancer prevention, in order to provide reference for relevant disease prevention and control research in the future.
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