Sima – an Open-source Simulation Framework for Realistic Large-scale Individual-level Data Generation
Tikka, S., Hakanen, J., Saarela, M., & Karvanen, J. (2021). Sima – an Open-source Simulation Framework for Realistic Large-scale Individual-level Data Generation. International Journal of Microsimulation, 14(3), 27-53. https://doi.org/10.34196/IJM.00240
Published inInternational Journal of Microsimulation
DisciplineHuman and Machine based Intelligence in LearningTietotekniikkaKoulutusteknologia ja kognitiotiedeTilastotiedeMultiobjective Optimization GroupLaskennallinen tiedeResurssiviisausyhteisöHuman and Machine based Intelligence in LearningMathematical Information TechnologyLearning and Cognitive SciencesStatisticsMultiobjective Optimization GroupComputational ScienceSchool of Resource Wisdom
© 2021, Tikka et al.
We propose a framework for realistic data generation and the simulation of complex systems and demonstrate its capabilities in a health domain example. The main use cases of the framework are predicting the development of variables of interest, evaluating the impact of interventions and policy decisions, and supporting statistical method development. We present the fundamentals of the framework by using rigorous mathematical definitions. The framework supports calibration to a real population as well as various manipulations and data collection processes. The freely available open-source implementation in R embraces efficient data structures, parallel computing, and fast random number generation, hence ensuring reproducibility and scalability. With the framework, it is possible to run daily-level simulations for populations of millions of individuals for decades of simulated time. An example using the occurrence of stroke, type 2 diabetes, and mortality illustrates the usage of the framework in the Finnish context. In the example, we demonstrate the data collection functionality by studying the impact of nonparticipation on the estimated risk models and interventions related to controlling excessive salt consumption. ...
PublisherInternational Microsimulation Association
ISSN Search the Publication Forum1747-5864
Publication in research information system
MetadataShow full item record
Related funder(s)Academy of Finland
Funding program(s)Research profiles, AoF
Additional information about fundingThis work belongs to the thematic research area “Decision analytics utilizing causal models and multiobjective optimization” (DEMO) supported by Academy of Finland (grant number 311877).
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