A1 Journal article (refereed)
Sima – an Open-source Simulation Framework for Realistic Large-scale Individual-level Data Generation (2021)

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

JYU authors or editors

Publication details

All authors or editors: Tikka, Santtu; Hakanen, Jussi; Saarela, Mirka; Karvanen, Juha

Journal or series: International Journal of Microsimulation

eISSN: 1747-5864

Publication year: 2021

Publication date: 31/12/2021

Volume: 14

Issue number: 3

Pages range: 27-53

Publisher: International Microsimulation Association

Publication country: United Kingdom

Publication language: English

DOI: https://doi.org/10.34196/IJM.00240

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/80356


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.

Keywords: statistical methods; mathematical models; data systems; data structures; data processing; health sector; simulation; forecasts; source codes; open source code

Contributing organizations

Related projects

Ministry reporting: Yes

Reporting Year: 2022

Preliminary JUFO rating: 1

Last updated on 2022-20-09 at 14:46