Skip to main content

Spatio-temporal Simulation of Epidemiological SIQR Model Based on the Multi-Agent System with Focus on Influenza A (H1N1)

  • Conference paper
Book cover Computational Intelligence and Intelligent Systems (ISICA 2010)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 107))

Included in the following conference series:

Abstract

Influenza A (H1N1) has caused tremendous damage in the world, so to learn its law is of great significance in the epidemic prevention and social stability. Taking the multi-agent system (MAS), geo-spatial environment to build the simulation model, SIQR epidemic model is introduced to simulate the process of the spread of influenza A (H1N1), test the multiple sets of preventive and control measures proposed from the perspectives of administrators and the public and do comparisons with the testing results. The testing results indicate that: the control of short-term epidemic spread requires administrators to implement powerful effective measures in public places to isolate patients, while inhibition of spread and rebound of the epidemic in a long-term way hereof needs administrators and the public working together to strengthen the self-protection, and timely medical treatment; repeated trials of the disease shows that the occurrence of rebound in the vicinity of 100d; the number of the immune and the susceptible are negatively correlated.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Kamel Boulos, M.N., Roudsari, A.V., Carson, E.R.: Health geomatics: An enabling suite of technologies in health and healthcare. J. Bio. Inf. 9, 195–219 (2001)

    Article  Google Scholar 

  2. Bates, J.M., Granger, C.W.J.: Combination of forecasts. Opelations Research uarterly 4, 451–468 (1969)

    Google Scholar 

  3. Brody, H., Rip, M., Vinten-Johansen, P., et al.: Map-making and myth-making in Broad Street: the London cholera epidemic 1854. The Lancet 356, 64–68 (2000)

    Article  Google Scholar 

  4. Sargent, D.J.: Comparison of Artificial Neural Networks with other statistical approaches. Cancer 8, 1636–1644 (2001)

    Article  Google Scholar 

  5. Kao, J., Huang, S.: Forecasts using neural network versus Box-Jenkins’s methodology for ambient air quality monitoring data. Air Waste Manag Assnc. 2, 219–226 (2000)

    Article  Google Scholar 

  6. Rodrigo, M.J., Morell, F., Helm, R.M., et al.: Identification and partial characterization of the soybean-dust allergens involved in the Barcelona asthma epidemic. J. Allergy Clin. Immunol. 4, 78–84 (1990)

    Google Scholar 

  7. Sanden, A., Jarvholm, B., Larsson, S., et al.: The risk of lung cancer and mesothelioma after cessation of asbestos exposure: a prospective cohort study of shipyard workers. Eur. Respir. 5, 281–285 (1992)

    Google Scholar 

  8. Dong-qing, Y.E.: Pandemic and response of influenza A (H1N1). Chinese Journal of Disease Control and Prevention 3, 216–218 (2009)

    Google Scholar 

  9. John Oommen, B., Calitoiu, D.: Modeling and simulating a disease outbreak by learning a contagion parameter-based model. In: Proceedings of the 2008 Spring simulation multiconference. Society for Computer Simulation International, Ottawa, Canada, pp. 14–17 (2008)

    Google Scholar 

  10. Macal, C.M., North, M.J.: Tutorial on agent-based modeling and simulation part 2: how to model with agents. In: Perrone, L.F., Lawson, B.G., Liu, J., Wieland, F.P. (eds.) Proceedings of the 37th Winter Simulation Conference, Monterey, CA (2006)

    Google Scholar 

  11. Bian, L.: A conceptual framework for an individual-based spatially explicit epidemiological model. Environment and Planning B: Planning and Design 31, 381–395 (2004)

    Article  Google Scholar 

  12. Chebeane, H., Echalier, F.: Towards the use of a multi-agents event based design to improve reactivity of production system. Computers & Industrial Engineering 37, 9–13 (1999)

    Article  Google Scholar 

  13. National Bureau of Statistics of China, http://som.xjtu.edu.cn/somlab/zhonguotong-jinianjian/2009/indexce.htm

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Xiao, H., Tian, H., Shao, L., Zhao, J., Xu, Jz. (2010). Spatio-temporal Simulation of Epidemiological SIQR Model Based on the Multi-Agent System with Focus on Influenza A (H1N1). In: Cai, Z., Tong, H., Kang, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2010. Communications in Computer and Information Science, vol 107. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16388-3_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-16388-3_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16387-6

  • Online ISBN: 978-3-642-16388-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics