Abstract
In the coming decades, the size and age-structure of Europe’s population will undergo dramatic changes due to low fertility rates and continuous increases in life expectancy. These changes also bring significant impacts on the economies of these countries. The impacts are a shortage of workers, chronic and degenerative diseases, increased government spending on health care and pensions. The more precise idea of the economic impacts countries have, the better they can over time develop strategies to deal with the situation. The aim of this paper is to present a dynamic simulation modelling as a tool to illustrate the economic aspects of population aging. The purpose of the simulation model is to simulate the behavior of the real system. The simulation model mimics when they run the substantial sites of the modelled system. The key to creating a simulation model is to understand the relationships and constraints of the modelled object. In the context of the research the simulation model is used for diseases in old age, particularly dementia. A tool for the simulation model is a software called STELLA, which meets the requirements for this area. The created and proposed model shows a number of benefits that are important for the expression of the economic aspects of diseases in the old age. These benefits are not included in standard statistical methods for predicting future development and other economic analyses used for this purpose.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
AnyLogic. (2014). Use of simulation modeling. Multimethod simulation software, [online] Available at http://www.anylogic.com/use-of-simulation. Accessed 22 July 2014.
Bures, V. (2011). Systémové myšlení pro manažery [Systems thinking for managers]. Praha: Professional Publishing.
Bury, A. (2007). Teorie systému a řízení [Theory and system management]. Ostrava: Vysoká škola bánská, Technická univerzita.
BusinessInfo.cz. (2009). Predikce vývoje prostředí a proces strategické analýzy [Prediction of the development environment and process of strategic analysis], [online] Available at http://www.businessinfo.cz/cs/clanky/predikce-prostredi-strategicka-analyza-2803.htnajít. Accessed 22 July 2014.
Dlouhy, M., Fabry, J., & Kuncova, M. (2005). Simulace pro economy [Simulation for the economy]. Praha: VŠE.
Gallerani, A., & Sanchez, H. (2012). Method for simulating complex systems in e.g., economics field, involves integrating non-subscripted and subscripted system dynamics models. Korean Journal of Chemical Engineering, 29(4), 444–451.
Guloglu, B., & Tekin, R. B. (2012). Panel causality analysis of the relationship among research and development, innovation, and economic growth in high-income OECD countries. Eurasian Economic Review, 2(1), 32–47.
He, D., & Bechhoefer, E. (2008). Development and validation of bearing diagnostic and prognostic tools using HUMS condition indicators. Proceedings of the IEEE Aerospace Conference, Big Sky, MT.
Jowit, J. (2013). Ageing population will have huge impact on social services. The Guardian, [online] Available at http://www.theguardian.com/society/2013/feb/24/britain-ageing-population-lords-inquiry. Accessed 22 July 2014.
Kvasnicka, V., & Pospíchal, J. (2005). Informatika pre sociálne vedy [Informatics for social sciences]. Bratislava: Univerzita Komenského.
Maresova, P., Kacetl, J., Stemberkova, R., & Kuca, K. (2015, January 22). Care For Czech Republic’s ageing population. Conference of Modern and Current Trends in the Public Sector Research, Brno Slapanice.
Maresova, P., Mohelska, H., & Kuca, K. (2014). Economics aspects of ageing population. 3rd World Conference on Business, Economics and Management. Procedia—Social and Behavioral Sciences.
Mosallam, A., Medjaher, K., & Zerhouni, N. (2013). Nonparametric time series modelling for industrial prognostics and health management. The International Journal of Advanced Manufacturing Technology, 69(5), 1685–1699.
NIH. (National Institute on Aging). (2011). New disease patterns, [online] Available at http://www.nia.nih.gov/research/publication/global-health-and-aging/new-disease-patterns. Accessed 12 July 2014.
Pokorny, M. (1996). Umělá inteligence v modelování a řízení [Artificial intelligence in modeling and control]. Praha: BEN—technická literatura.
Powell, J. D., Franklin, G. F., & Naeini, A. E. (2006). Feedback control of dynamic systems (5th ed.). Upper Saddle River, NJ: Prentice Hall.
Roemer, M., Byington, C., Kacprzynski, G., & Vachtsevanos, G. (2013). An overview of selected prognostic technologies with reference to an integrated PHM architecture. Proceedings of the First International Forum on Integrated System Health Engineering and Management in Aerospace (pp. 1–9). Barcelona, Spain.
Sterman, J. D. (1991). A skeptic’s guide to computer models, [online]. Available at http://www.systemdynamics.org/conferences/1985/proceed/sterm852.pdf. Accessed 2 July 2014.
Uckun, S., Goebel, K., & Lucas, P. J. F. (2008, May 23). Standardizing research methods for prognostics. International Conference on Prognostics and Health Management (PHM08), IEEE, Denver.
WHO. (2012). Global health and aging, [online] Available at http://www.who.int/ageing/publications/global_health.pdf. Accessed 29 Sep 2014.
Acknowledgments
This paper was supported by the research project SPEV—Economic and Managerial Aspects of Processes in Biomedicine, University of Hradec Kralove, Faculty of Informatics and Management, 2014.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Maresova, P., Tomaskova, H., Kuca, K. (2016). The Use of Simulation Modelling in the Analysis of the Economic Aspects of Diseases in Old Age. In: Bilgin, M., Danis, H., Demir, E., Can, U. (eds) Business Challenges in the Changing Economic Landscape - Vol. 1. Eurasian Studies in Business and Economics, vol 2/1. Springer, Cham. https://doi.org/10.1007/978-3-319-22596-8_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-22596-8_26
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22595-1
Online ISBN: 978-3-319-22596-8
eBook Packages: Economics and FinanceEconomics and Finance (R0)