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Simulation Approach to Forecasting Population Ageing on Regional Level

  • Jacek ZabawaEmail author
  • Bożena Mielczarek
  • Maria Hajłasz
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 657)

Abstract

The paper discusses the simulation model that uses system dynamics method to study the demographic changes forecasted for the population, based on the aging chain approach. The goal of the study was to elaborate the method to overcome the drainage problem that manifests itself in the smaller number of individuals belonging to the simulated cohorts, as compared to the real population data. The solution for the drainage problem is presented. We propose the original modelling methodology that assumes the coexistence of main and elementary population cohorts. The simulation model was verified based on the historical data for Wrocław Region population and the results of the experiments prove the high degree of compatibility of the simulated age and gender related characteristics with the empirical data, which entitles us to formulate the perspectives of using this approach in the next stages of our research.

Keywords

Simulation System dynamics Ageing chain Population 

Notes

Acknowledgements

This project was financed by the grant Simulation modeling of the demand for healthcare services from the National Science Centre, Poland, and was awarded based on the decision 2015/17/B/HS4/00306.

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References

  1. 1.
    Ansah, J.P., Eberlein, R.L., Love, S.R., Bautista, M.A., Thompson, J.P., Malhotra, R., Matchar, D.B.: Implications of long-term care capacity response policies for an aging population: a simulation analysis. Health Policy 116(1), 105–113 (2014)CrossRefGoogle Scholar
  2. 2.
    Diamond, B., Krahl, D., Nastasi, A., Tag, P.: Extendsim advanced techology: integrated simulation database. In: Johansson, B., Jain, S., Montoya-Torres, J., Hugan, J., Yücesan, E. (eds.) Proceedings of the Winter Simulation Conference (WSC), pp. 32–39. IEEE (2010)Google Scholar
  3. 3.
    Eberlein, R.L., Thompson, J.P., Matchar, D.B.: Chronological aging in continuous time. In: Husemann, E., Lane, D. (eds.) Proceedings of the 30th International Conference of the System Dynamics Society, St. Gallen, Switzerland (2012)Google Scholar
  4. 4.
    Eberlein, R.L., Thompson, J.P.: Precise modeling of aging populations. Syst. Dyn. Rev. 29(2), 87–101 (2013)CrossRefGoogle Scholar
  5. 5.
    Forrester, J.W.: Industrial dynamics-after the first decade. Manag. Sci. 14(7), 398–415 (1968)CrossRefGoogle Scholar
  6. 6.
    GUS Główny Urząd Statystyczny. http://www.stat.gov.pl. Accessed Jan 2017
  7. 7.
    Jagger, C., Matthews, R., Lindesay, J., Robinson, T., Croft, P., Brayne, C.: The effect of dementia trends and treatments on longevity and disability: a simulation model based on the MRC cognitive function and ageing study (MRC CFAS). Age Ageing 38(3), 319–325 (2009)CrossRefGoogle Scholar
  8. 8.
    Krahl, D.: Extend: the extend simulation environment. In: Yücesan, E., Chen, C.-H., Snowdom, J., Charnes, J. (eds.) Proceedings of the 2002 Winter Simulation Conference: Exploring New Frontiers, pp. 205–213 (2002)Google Scholar
  9. 9.
    Krahl, D.: Extendsim 7. In: Mason, S., Hill, R., Mönch, L., Rose, O., Jefferson, T., Fowler, J. (eds.) Proceedings of the 2008 Winter Simulation Conference, pp. 215–221 (2008)Google Scholar
  10. 10.
    Krahl, D.: Extendsim 9. In: Pasupathy, R., Kim, S.-H., Tolk, A., Hill, R., Kuhl, M. (eds.) Proceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World, pp. 4065–4072. IEEE Press, Piscataway (2013)Google Scholar
  11. 11.
    Lagergren, M.: What happened to the care of older persons in Sweden? A retrospective analysis based upon simulation model calculations, 1985–2000. Health Policy 74(3), 314–324 (2005)CrossRefGoogle Scholar
  12. 12.
    Loumrhari, G.: Ageing, longevity and savings: the case of Morocco. Int. J. Econ. Finan. Issues 4(2), 344–352 (2014)Google Scholar
  13. 13.
    Mielczarek, B., Zabawa, J.: Modeling healthcare demand using a hybrid simulation approach. In: Roeder, T., Frazier, P., Szechtman, R., Zhou, E., Huschka, T., Chick, S. (eds.) Proceedings of the 2016 Winter Simulation Conference, pp. 1535–1546. IEEE Press, Piscataway (2016)Google Scholar
  14. 14.
    Mielczarek, B., Zabawa, J.: Modelling population growth, shrinkage and aging using a hybrid simulation approach: application to healthcare. In: Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH, vol. 1, pp. 75–83. Scitepress (2016)Google Scholar
  15. 15.
    Population Modeling Working Group: Population modeling by examples (WIP). In: Proceedings of the Symposium on Modeling and Simulation in Medicine (MSM 2015), pp. 61–66. Society for Computer Simulation International, San Diego, CA, USA (2015). http://dl.acm.org/citation.cfm?id=2887741#authors
  16. 16.
    Sato, J., Chalise, N., Hovmand, P., Zoellner, N., Carson, K., Brown, A.: Birth cohorts approach to modeling aging populations. In: 33rd International Conference of the System Dynamics Society 2015, Cambridge, Massachusetts, pp. 2882–2889 (2015)Google Scholar
  17. 17.
    Senese, F., Tubertini, P., Mazzocchetti, A., Lodi, A., Ruozi, C., Grilli, R.: Forecasting future needs and optimal allocation of medical residency positions: the Emilia-Romagna region case study. Hum. Resour. Health 13(7) (2015). doi: 10.1186/1478-4491-13-7
  18. 18.
    Tian, Y., Zhao, X.: Stochastic forecast of the financial sustainability of basic pension in China. Sustainability 8(46) (2016). doi: 10.3390/su8010046

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Jacek Zabawa
    • 1
    Email author
  • Bożena Mielczarek
    • 1
  • Maria Hajłasz
    • 1
  1. 1.Wrocław University of Science and TechnologyWrocławPoland

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