1. Proceedings
  2. I3M
  3. 2020
  4. EMSS
  5. 10.46354/i3m.2020.emss.028

Analysis of obesity epidemic modelling: is there a solution?

  • a Maja Atanasijvić-Kunc ,
  • b Tina Sentočnik
  • a University of Ljubljana, Faculty of Electrical Engineering, Tržaška 25, 1000 Ljubljana, Slovenia
  • b Medico dr. Sentočnik, d.o.o., Levčeva ulica 11, 1000 Ljubljana, Slovenia
Cite as
Atanasijvić-Kunc M., Sentočnik T. (2020). Analysis of obesity epidemic modelling: is there a solution?. Proceedings of the 32nd European Modeling & Simulation Symposium (EMSS 2020), pp. 201-206. DOI: https://doi.org/10.46354/i3m.2020.emss.028

Abstract

Overweight and obesity have reached pandemic extensions. The World Health Organization has recognized obesity being a disease which means it has to be treated and if/when possible also prevented. At first glance the solution of the problem seems to be very simple, however, the rising prevalence of this disease proves that the problem is much more demanding and is connected with all pores of our lives. Through this work we tried to analyze some epidemiological mathematical and/or simulation models related to the problem of obesity and accompanying processes regarding different goals of models’ usage. Of a special importance was the informativeness of modelling results indicating social and/or economic burdens and the efficacy in estimation of the observed problems’ solution(s). It was detected that a great potential have multi-model descriptions which enable for each of the sub-processes and/or sub-steps the presentation of only the most important causal interactions. Efficient and correspondingly evaluated mathematical description incorporating also control activities is still a challenge waiting for a successive practical realization.

References

  1. Andersen, L. G., Baker, J. L., Sørensen, T. I. A. (2012). Contributions of Incidence and Persistence to the Prevalence of Childhood Obesity during the Emerging Epidemic in Denmark. PLoS ONE, 7(8), e42521:1-8.
  2. Atanasijević-Kunc, M., Drinovec, J., Ručigaj, S., Mrhar, A. (2008a). Modelling of the Risk Factors and Chronic Diseases that Influence the Development of Serious Health Complications. Slovenian Medical Journal, 77(8): 487–498.
  3. Atanasijević-Kunc, M., Drinovec, J., Ručigaj, S., Mrhar, A. (2008b). Modeling the influence of risk factors and chronic diseases on the development of strokes and peripheral arterial-vascular disease, Simulation modelling practice and theory, 16(8):998–1013.
  4. Atanasijević-Kunc, M. and Drinovec, J. (2011). Burden of diabetes type 2 through modelling and simulation. Topics in the prevention, treatment and complications of type 2 diabetes. InTech., Rijeka, 3–28.
  5. Atanasijević-Kunc, M., Drinovec, J., Ručigaj, S., Mrhar, A. (2011). Simulation analysis of coronary heart disease, congestive heart failure and end-stage renal disease economic burden. Mathematics and computers in simulation, 82(3):494–507.
  6. Atanasijević-Kunc, M., Drinovec, J., Sentočnik, T. (2013). Burdens of obesity: multi-model
    description. Simulation notes Europe, 23(2):85–92.
  7. Berghöfer, A., Pischon, T., Reinhold, T., Apovian, C. M., Sharma, A. M., Willich, S. N. (2008). Obesity prevalence from a European perspective: a systematic review. BMC Public Health, 8(200)
  8. Bruzzone, A. G., Novak, V., Madeo, F. (2012). Agent Based Simulation Model for Obesity Epidemic Analysis. Proceedings of the International Workshop on Innovative Simulation for Health Care, Vienna, Austria, 209–217.
  9. Christakis, N. A. and Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. N Engl J Med, 357:370–379.
  10. Dreifus, C. (2012). A Mathematical Challenge to Obesity, The New York Times, May 14.
  11. Ejima, K., Aihara, K., Nishiura, H. (2013). Modeling the obesity epidemic: social contagion and its implications for control. Theoretical Biology and Medical Modelling, 10(17):1-13.
  12. Ejima, K., Thomas, D., Allison, D. B. (2018). A Mathematical Model for Predicting Obesity
    Transmission With Both Genetic and Nongenetic Heredity. Obesity, 26(5):927–933.
  13. Frank, A. (2014) Why is so difficult to lose weight? Am J Lifestyle Med., 8(5):318–323.
  14. Hales, C. M., Carroll, M. D., Fryar, C. D., Ogden, C. L. (2017). Prevalence of Obesity Among Adults and Youth: United States, 2015–2016. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics, Data Brief, No. 288, October, 1–8.
  15. Hall, K. (2010). Mechanisms of Metabolic Fuel Selection Modeling Human Metabolism and Body-Weight Change. IEEE Engineering in Medicine and Biology Magazine, 36–41.
  16. Homer, J. B. and Hirsch, G. B. (2006). System Dynamics Modeling for Public Health: Background and Opportunities. American Journal of Public Health, 96(3):452–458.
  17. Huang, H., Yan, Z., Chen, Y., Liu, F. (2016). A social contagious model of the obesity epidemic. Scientific Reports, 6(37961):1-9.
  18. Keaver, L., Webber, L., Dee, A., Shiely, F., Marsh, T., Balanda, K., Perry, I. (2013). Application of the UK Foresight Obesity Model in Ireland: The Health and Economic Consequences of Projected Obesity Trends in Ireland. PLoS ONE, 8(11), e79827:1-8.
  19. Landi, A., Piaggi, P., Lippi, C., Santini, F., Pinchera, A. (2010). Statistical toolbox in medicine for predicting effects of therapies in obesity. Proceedings of the IEEE Workshop on Health Care Management (WHCM), Venice, Italy, 1-4.
  20. Lehnert, T., Sonntag, D., Konnopka, A., Riedel-Heller, S., König, H.-H. (2012). The long-term costeffectiveness of obesity prevention interventions: systematic literature review. Obesity Reviews, 13:537–553.
  21. Mokdad, A. H., Bowman, B. A., Ford, E. S., Vinicor, F., Marks, J. S., Koplan, J. P., (2001). The continuing epidemics of obesity and diabetes in the United States. JAMA, 286(10):1195-1200.
  22. Morales, A., Jódar, L., Gonzalez, G., Santonja, F. J., Villanueva, R. J., Rubio, C. (2008). Childhood
    Obesity in the Region of Valencia, Spain: Evolution and Prevention Strategies. J. Med. Sci., 8(8):715-721.
  23. Nawarycz, T., Pytel, K., Drygas, W., Gazicki-Lipman, M., Ostrowska-Nawarycz, L. (2013). A Fuzzy Logic Approach to the Evaluation of Health Risks Associated with Obesity. Proceedings of the 2013 Federated Conference on Computer Science and Information Systems, Kraków, 231–234.
  24. Ramirez-Nafarrate, A. and Gutierrez-Garcia, J. O. (2013). An Agent-Based Simulation Framework to Analyze the Prevalence of Child Obesity. Proceedings of the 2013 Winter Simulation Conference. R. Pasupathy, Washington, DC, USA, S.- H. Kim, A. Tolk, R. Hill, and M. E. Kuhl, eds., 2330-2339.
  25. Santonja, F. J., Villanueva, R. J., Jódar, L., GonzalezParra, G. (2010). Mathematical modelling of social obesity epidemic in the region of Valencia, Spain. Mathematical and Computer Modelling of Dynamical Systems, 16(1): 23-34.
  26. Sentočnik, T., Atanasijević-Kunc, M., Drinovec, J., Pfeifer, M. (2014). Efficacy analysis of a body-massreduction treatment using mathematical modelling. Mathematical and computer modelling of dynamical systems, 20(2):146–169.
  27. Shih, H.-C., Chou, P., Liu, C.-M., Tung, T.-H. (2007). Estimation of progression of multi-state chronic disease using the Markov model and prevalence pool concept. BMC Medical Informatics and Decision Making, 7(34).
  28. Swinburn, B., Gill, T., Kumanyika, S. (2005). Obesity prevention: a proposed framework for translating evidence into action. Obesity reviews, 6:23–33.
  29. Swinburn, B. A. (2008). Obesity prevention: the role of policies, laws and regulations. Australia and New Zealand Health Policy, 5(12).
  30. Thompson, D. and Wolf, A. M. (2001). The medical-care cost burden of obesity. Obesity reviews, 2:189–197.
  31. Vandevijvere, S., Chow, C. C., Hall, K. D., Umalia, E., Swinburn, B. A. (2015). Increased food energy supply as a major driver of the obesity epidemic: a global analysis. Bulletin of the World Health Organization, 93:446–456.
  32. Wang, Y. and Beydoun, M. A. (2007). The Obesity Epidemic in the United States — Gender, Age,
    Socioeconomic, Racial/Ethnic, and Geographic Characteristics: A Systematic Review and MetaRegression Analysis. Epidemiologic Reviews, 29:6–28.
  33. Wang, Y., Beydoun, M. A., Liang, L., Caballero, B., Kumanyika, S. K. (2008). Will All Americans
    Become Overweight or Obese? Estimating the Progression and Cost of the US Obesity Epidemic. Obesity, 16:2323–2330.
  34. WHO - World health organization (2017). Obesity and overweight, Fact sheet, Updated April 2020, accessed 2 July 2020, Available from: http://www.who.int/mediacentre/factsheets/fs311/
    en/