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We propose a framework for realistic data generation and simulation ofcomplex systems and demonstrate its capabilities in the health domain. The mainuse cases of the framework are predicting the development of risk factors anddisease occurrence, evaluating the impact of interventions and policydecisions, and statistical method development. We present the fundamentals ofthe framework using rigorous mathematical definitions. The framework supportscalibration to a real population as well as various manipulations and datacollection processes. The freely available open-source implementation in Rembraces efficient data structures, parallel computing and fast random numbergeneration which ensure reproducibility and scalability. With the framework itis possible to run daily-level simulations for populations of millions ofindividuals for decades of simulated time. An example on the occurrence ofstroke, type 2 diabetes and mortality illustrates the usage of the framework inthe Finnish context. In the example, we demonstrate the data-collectionfunctionality by studying the impact of non-participation on the estimated riskmodels and interventions related to controlling the additional salt intake.

Simulation Framework for Realistic Large-scale Individual-level Data Generation with an Application in the Health Domain