A Comparison Between a Deterministic, Compartmental Model and an Individual Based-stochastic Model for Simulating the Transmission Dynamics of Pandemic Influenza

Hung-Jui Chang, Jen-Hsiang Chuang, Tsurng-Chen Chern, Mart Stein, Richard Coker, Da-Wei Wang, Tsan-sheng Hsu

Abstract

Simulation models are often used in the research area of epidemiology to understand characteristics of disease outbreaks. As a result, they are used by authorities to better design intervention methods and to better plan the allocation of medical resources. Previous work make use of many different types of simulation models with an agent-based model, e.g., Taiwan simulation system, and an equation-based model, e.g., AsiaFluCap simulation system, being the two most popular ones. Some comparison studies has been attempted in the past to understand the limits, efficiency, and usability of some model. However, there was little studies to justify why one model is used instead of the other. In this paper, instead of studying the two most popular models one by one, we try to do a comparative study between these two most popular ones. By observing that one model can outperform the other in some cases, and vice versa, we hence study conditions that which one should be used. Furthermore, previous studies show little results in the issue of allocating medical resources. Our paper studies and compares the two models using medical resources allocation as one of our primary concerns. As a conclusion, we come out with a general guideline to help model designers to pick one that fits the given needs better.

References

  1. Ajelli, M., Gonc¸alves, B., Balcan, D., Colizza, V., Hu, H., J. J Ramasco, S. M., and Vespignani, A. (2010). Comparing large-scale computational approaches to epidemic modeling: Agent-based versus structured metapopulation models. BMC Infectious Diseases 10, 190 (2010).
  2. AsiaFluCap (2009). The AsiaFluCap Simulator. http://www.cdprg.org/asiaflucap-simulator.php.
  3. Berger, T. (2001). Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis. Agricultural Economics, 25(2-3):245 - 260.
  4. Bobashev, G. V., Goedecke, D. M., Yu, F., and Epstein, J. M. (2007). A hybrid epidemic model: combining the advantages of agent-based and equation-based approaches. In WSC 7807: Proceedings of the 39th conference on Winter simulation, pages 1532-1537, Piscataway, NJ, USA. IEEE Press.
  5. Britton, T. and Lindenstrand, D. (2009). Epidemic modelling: aspects where stochasticity matters. Mathematical Biosciences, 222(2):109-116.
  6. Connell, R., Dawson, P., and Skvortsov, A. (2009). Comparison of an agent-based model of disease propagation with the generalised SIR epidemic model. Science And Technology, 5(3):1-22.
  7. Davidsson, P. (2002). Agent based social simulation: A computer science view. J. Artificial Societies and Social Simulation, 5(1).
  8. Diekmann, O. and Heesterbeek, J. (2000). Mathematical epidemiology of infectious diseases: model building, analysis, and interpretation. Wiley series in mathematical and computational biology. John Wiley.
  9. Garnett, G. P. (2002). An introduction to mathematical models in sexually transmitted disease epidemiology. Sex Transm Infect, 78(1):7-12.
  10. Keeling, M. J. and Danon, L. (2009). Mathematical modelling of infectious diseases. British Medical Bulletin, 92(1):33-42.
  11. Krumkamp, R., Kretzschmar, M., Rudge, J. W., Ahmad, A., Hanvoravongchai, P., Westenhoefer, J., STEIN, M., Putthasri, W., and Coker, R. (2011). Health service resource needs for pandemic influenza in developing countries: a linked transmission dynamics, interventions and resource demand model. Epidemiology and Infection, 139:59-67.
  12. Lunelli, A., Pugliese, A., and Rizzo, C. (2009). Epidemic patch models applied to pandemic influenza: contact matrix, stochasticity, robustness of predictions. Mathematical Biosciences, 220(1):24-33.
  13. Macal, C. M. and North, M. J. (2005). Tutorial on agentbased modeling and simulation. In Winter Simulation Conference, pages 2-15.
  14. Moss, S. and Davidsson, P., editors (2001). Multi-AgentBased Simulation, Second International Workshop, MABS 2000, Boston, MA, USA, July, 2000, Revised and Additional Papers, volume 1979 of Lecture Notes in Computer Science. Springer.
  15. Parker, D. C., Manson, S. M., Janssen, M. A., Hoffmann, M. J., and Deadman, P. (2003). Multi-agent systems for the simulation of land-use and land-cover change: A review. Annals of the Association of American Geographers, 93(2):314337.
  16. Rahmandad, H. and Sterman, J. (2008). Heterogeneity and network structure in the dynamics of diffusion: Comparing agent-based and differential equation models. Management Science, 54:998-1014.
  17. Rudge, J. W., Hanvoravongchai, P., Krumkamp, R., Chavez, I., Adisasmito, W., Chau, P. N., Phommasak, B., Putthasri, W., Shih, C.-S., Stein, M., Timen, A., Touch, S., Reintjes, R., Coker, R., and on behalf of the AsiaFluCap Project Consortium (2012). Health system resource gaps and associated mortality from pandemic influenza across six asian territories. PLoS ONE, 7(2):e31800.
  18. Stein, M., Rudge, J., Coker, R., Weijden, C., Krumkamp, R., Hanvoravongchai, P., Chavez, I., Putthasri, W., Phommasack, B., Adisasmito, W., Touch, S., Sat, L., Hsu, Y.-C., Kretzschmar, M., and Timen, A. (2012). Development of a resource modelling tool to support decision makers in pandemic influenza preparedness: The asiaflucap simulator. BMC Public Health, 12(1):870.
  19. Tsai, M., Chern, T., Chuang, J., Hsueh, C., Kuo, H., Liau, C., Riley, S., Shen, B., Wang, D., Shen, C., and Hsu, T. (2010). Efficient simulation of the spatial transmission dynamics of influenza. PloS ONE, 5(11):1-8.
Download


Paper Citation


in Harvard Style

Chang H., Chuang J., Chern T., Stein M., Coker R., Wang D. and Hsu T. (2014). A Comparison Between a Deterministic, Compartmental Model and an Individual Based-stochastic Model for Simulating the Transmission Dynamics of Pandemic Influenza . In Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH, ISBN 978-989-758-038-3, pages 586-594. DOI: 10.5220/0005040905860594


in Bibtex Style

@conference{simultech14,
author={Hung-Jui Chang and Jen-Hsiang Chuang and Tsurng-Chen Chern and Mart Stein and Richard Coker and Da-Wei Wang and Tsan-sheng Hsu},
title={A Comparison Between a Deterministic, Compartmental Model and an Individual Based-stochastic Model for Simulating the Transmission Dynamics of Pandemic Influenza},
booktitle={Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,},
year={2014},
pages={586-594},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005040905860594},
isbn={978-989-758-038-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,
TI - A Comparison Between a Deterministic, Compartmental Model and an Individual Based-stochastic Model for Simulating the Transmission Dynamics of Pandemic Influenza
SN - 978-989-758-038-3
AU - Chang H.
AU - Chuang J.
AU - Chern T.
AU - Stein M.
AU - Coker R.
AU - Wang D.
AU - Hsu T.
PY - 2014
SP - 586
EP - 594
DO - 10.5220/0005040905860594