Dynamic disease risk prediction models are essential for precision prevention strategies. Over the last twenty years, there has been a surge in research focused on these models for precision prevention. However, widely used models（static models） often overlook the impact of changes in predictors over time on disease risk, leading to inevitable calibration drift. This paper reviewed modeling methods for dynamic risk prediction models and provided reference for their development. The conclusions are as follows: As healthcare big data becomes more interconnected and shared, and new methods of statistics and artificial intelligence emerge, the challenge lies in discovering richer predictors, in identifying more accurate modes of action, and in creating interpretable disease risk prediction models which align with biomedical contexts and practical scenarios, to enhance common prevention of common diseases and co-prevention of heterogeneous diseases and to achieve precision and personalized prevention across a spectrum of diseases. This will be a crucial focus for future research on predictive modeling methodologies.

prediction model  /  static model  /  dynamic prediction  /  precision prevention