Simulations for Epidemiology and Public Health Education

  • C.-Y. Huang
  • Y.-S. Tsai
  • T.-H. Wen
Part of the The OR Essentials series book series (ORESS)


Recent and potential outbreaks of infectious diseases are triggering interest in predicting epidemic dynamics on a national scale and testing the efficacies of different combinations of public health policies. Network-based simulations are proving their worth as tools for addressing epidemiology and public health issues considered too complex for field investigations and questionnaire analyses. Universities and research centres are therefore using network-based simulations as teaching tools for epidemiology and public health education students, but instructors are discovering that constructing appropriate network models and epidemic simulations are difficult tasks in terms of individual movement and contact patterns. In this paper we will describe (a) a four-category framework (based on demographic and geographic properties) to discuss ways of applying network-based simulation approaches to undergraduate students and novice researchers; (b) our experiences simulating the transmission dynamics of two infectious disease scenarios in Taiwan (HIV and influenza); (c) evaluation results indicating significant improvement in student knowledge of epidemic transmission dynamics and the efficacies of various public health policy suites; and (d) a geospatial modelling approach that integrates a national commuting network as well as multi-scale contact structures.


Public Health Policy Transmission Dynamic Contact Pattern Stage Duration Public Health Expert 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Aldrich C (2004). Simulations and the Future of Learning: An Innovative (and Perhaps Revolutionary) Approach to e-Learning. Pfeiffer: San Francisco, CA.Google Scholar
  2. Alfonseca M, Martinez-Bravo MT and Torrea JL (2000). Mathematical models for the analysis of Hepatitis B and AIDS epidemics. Simulation 74 (4): 219–226.CrossRefGoogle Scholar
  3. Axelrod R (1997). Advancing the art of simulation in the social sciences. Complexity 3 (2): 16–22.CrossRefGoogle Scholar
  4. Barrett CL, Eubank SG and Smith JP (2003). If smallpox strikes Portland. Sci Am 292 (3): 42–49.Google Scholar
  5. Bertsche D, Crawford C and Macadam SE (1996). Is simulation better than experience. McKinsey Quart 1 (1): 50–58.Google Scholar
  6. Bruner JS and Lufburrow RA (1963). The process of education. Am J Phys 31: 468.CrossRefGoogle Scholar
  7. Boccara N and Cheong K (1993). Critical-behavior of a probabil istic-automata network SIS model for the spread of an infectious-disease in a group of moving individuals. J Phys A-Math Gen 26: 3707–3717.CrossRefGoogle Scholar
  8. Colpitts BG (2002). Teaching transmission lines: A project of measurement and simulation. IEEE T Educ 45 (3): 245–252.CrossRefGoogle Scholar
  9. Directorate General of Budget, Accounting and Statistics (2006). Social indicators, Executive Yuan, Republic of China.Google Scholar
  10. Ferguson NM et al (2005). Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature 437 (7056): 209–214.CrossRefGoogle Scholar
  11. Gilbert GN and Troitzsch KG (1999). Simulation for the Social Scientist. Open University Press: Philadelphia, PA.Google Scholar
  12. Hargrave CP and Kenton JM (2000). Preinstructional simulations: Implications for science classroom teaching. J Comput Math Sci Teach 19 (1): 47–58.Google Scholar
  13. Hsieh JL, Huang CY, Sun CT and Chen YMA (2005). Using the CAMIM small-world epidemic model to analyze public health policies. In: Proceedings of Western Simulation Multiconference on Health Sciences Simulation. New Orleans, Louisiania, USA, pp 63–69. The Society for Modeling and Simulation International: San Diego, California, USA.Google Scholar
  14. Hsieh JL, Sun CT, Kao GYM and Huang CY (2006). Teaching through simulation: Epidemic dynamics and public health policies. Simulation 82 (11): 731–759.CrossRefGoogle Scholar
  15. Huang CY, Sun CT, Hsieh JL and Lin H (2004). Simulating SARS: Small-world epidemiological modelling and public health policy assessments. JASSS7 (4),
  16. Huang CY, Sun CT and Lin HC (2005a). Influence of local information on social simulations in small-world network models. JASSS 8(4),
  17. Huang CY, et al (2005b). A novel small-world model: Using social mirror identities for epidemic simulations. Simulation 81 (10): 671–699.CrossRefGoogle Scholar
  18. Institute of Transportation, Executive Yuan, Republic of China (2008). General profile of respective transportation and communications sectors-RAILWAY, IOT–Transportation Information-Statistical Trend, Scholar
  19. Kao RR, Danon L, Green DM and Kiss IZ (2006). Demographic structure and pathogen dynamics on the network of livestock movements in Great Britain. P Roy Soc B: Biol Sci 273 (1597): 1999–2007.CrossRefGoogle Scholar
  20. Klein CA, Berlin LS, Kostolansky TJ and Del Palacio JR (2004). Stock simulation Engine for an Options Trading Game, Issued on March 23, 2003. United States Patent No. 6709330.Google Scholar
  21. Levy M, Levy H and Solomon S (1995). Microscopic simulation of the stock market: The effect of microscopic diversity. J Phys I 5: 1087–1107.Google Scholar
  22. Liao YH and Sun CT (2001). An educational genetic algorithms learning tool. IEEE T Educ 44 (2): 20.Google Scholar
  23. Longini Jr IM et al (2005). Containing pandemic influenza at the source. Science 309 (5737): 1083–1087.CrossRefGoogle Scholar
  24. Moore C and Newman MEJ (2000). Epidemics and percolation in small-world networks. Phys Rev E 61 (5): 5678–5682.CrossRefGoogle Scholar
  25. Oehme F (2000). Learn by doing: How to include new requirements of research in engineering education. Eur J Eng Educ 25 (2): 131–137.CrossRefGoogle Scholar
  26. Piaget J (1978). The Development of Thought: Equilibration of Cognitive Structures. Viking Press: New York, NY.Google Scholar
  27. Savery JR and Duffy TM 1995. Problem based learning: An instructional model and its constructivist framework. Educ Technol 35 (5): 31–38.Google Scholar
  28. Schneeberger A et al (2004). Scale-free networks and sexually transmitted diseases: A description of observed patterns of sexual contacts in Britain and Zimbabwe. Sex Transm Dis 31 (6): 380–387.CrossRefGoogle Scholar
  29. Stroud P et al (2007). Spatial dynamics of pandemic influenza in a massive artificial society. JASSS10 (4),
  30. Sumodhee CJ, et al (2005). Impact of social behaviors on HIV epidemic: A computer simulation view. In: Proceedings of International Conference on Computational Intelligence for Modelling, Control and Automation, Vienna, Austria 2: 550–556. IEEE Computer Society: Los Alamitos, CA, USA.Google Scholar
  31. Wenglinsky H 1998. Does it Compute? The Relationship Between Educational Technology and Student Achievement in Mathe matics. Educational Testing Service: Princeton, NJ.Google Scholar
  32. World Health Organization (WHO) (2003). HIV/AIDS in Asia and the Pacific region.–9821–43BE–9B73–B3444A3DADE6/0/HIV_AIDS_Asia_Pacific_Region2003.pdf.Google Scholar
  33. WHO (2007). Ten things you need to know about pandemic influenza, Scholar
  34. WHO (2008). Confirmed Human Cases of Avian Influenza A (H5N1), Scholar

Copyright information

© Operational Research Society 2016

Authors and Affiliations

  • C.-Y. Huang
    • 1
  • Y.-S. Tsai
    • 2
  • T.-H. Wen
    • 3
  1. 1.Chang Gung UniversityTaoyuanTaiwan
  2. 2.National Chiao Tung UniversityHsinchuTaiwan
  3. 3.National Taiwan UniversityTaipeiTaiwan

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