Abstract: Large-scale epidemiologic studies are rapidly leading to novel findings of risk factors associated with various human diseases. The increasing availability of multi-modal health data provides us with major opportunities to develop data integration methods for developing advanced risk prediction tools incorporating a rich set of risk factors, which could generate more effective strategies for disease prevention on healthy individuals and treatment strategies for patients. Such data fusion has been an understudied area with many open questions. In this talk, I will present some of my recent work on data integration methods for risk model development with two specific examples. The first example focuses on integrating individual- and summary-level information from studies on different types of risk factors and community-level pandemic dynamics to develop individualized prediction models for COVID-19 mortality risk. Such a methodological framework can be applied to predict and validate the risk of other diseases on both individual- and community-level and be continuously updated once new datasets or information are available. In the second application, we develop enhanced genome-wide polygenic risk prediction models for the underrepresented non-European populations by appropriately borrowing information across ancestries through the integration of ancestry-specific genetic datasets. Both applications demonstrate future promise of data integration methods for developing comprehensive risk models and informing targeted disease prevention strategies.