Gryphon: A Hybrid Agent-Based Modeling and Simulation Platform for Infectious Diseases

  • Bin Yu
  • Jijun Wang
  • Michael McGowan
  • Ganesh Vaidyanathan
  • Kristofer Younger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6007)


In this paper we present Gryphon, a hybrid agent-based stochastic modeling and simulation platform developed for characterizing the geographic spread of infectious diseases and the effects of interventions. We study both local and non-local transmission dynamics of stochastic simulations based on the published parameters and data for SARS. The results suggest that the expected numbers of infections and the timeline of control strategies predicted by our stochastic model are in reasonably good agreement with previous studies. These preliminary results indicate that Gryphon is able to characterize other future infectious diseases and identify endangered regions in advance.


Group Agent Stochastic Simulation Severe Acute Respiratory Syndrome Simulation Platform Secondary Group 
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. 1.
    Anderson, R.M., May, R.M.: Infectious Diseases of Humans: Dynamics and Control. Oxford University Press, Oxford (1992)Google Scholar
  2. 2.
    Rvachev, L.A., Longini, I.M.: A mathematical model for the global spread of influenza. Mathematical BioSciences 75, 3–22 (1985)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Sattenspiel, L., Simon, C.P.: The spread and persistence of infectious diseases in structured populations. Mathematical BioSciences 90, 341–366 (1988)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Eubank, S., Guclu, H., Kumar, V.S.A., Marathe, M., Srinivasan, A., Toroczkai, Z., Wang, N.: Modelling disease outbreaks in realistic urban social networks. Nature 429, 180–184 (2004)CrossRefGoogle Scholar
  5. 5.
    Colizza, V., Barrat, A., Barthelemy, M., Valleron, A.J., Vespignani, A.: Modeling the worldwide spread of pandemic influenza: Baseline case and containment intervention. PLOS Medicine (2007)Google Scholar
  6. 6.
    Hufnagel, L., Brockmann, D., Geisel, T.: Forecast and control of epidemics in a globalized world. PNAS 101(42), 14124–15129 (2004)CrossRefGoogle Scholar
  7. 7.
    Chowell, G., Fenimore, P.W., Castillo-Garsow, M.A., Castillo-Chavez, C.: SARS outbreaks in ontario, hong kong and singapore: the role of diagnosis and isolation as a control mechanism. Journal of Theorectical Biology 224, 1–8 (2003)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Chowell, G., Castillo-Chavez, C., Fenimore, P.W., Kribs-Zaleta, C.M., Arriola, L., Hyman, J.M.: Model parameters and outbreak control for SARS. Emerging Infectious Diseases 10(7), 1258–1263 (2004)Google Scholar
  9. 9.
    Feller, W.: An Introduction to Probability Theory and Its Applications, vol. 1. Wiley, Chichester (1968)zbMATHGoogle Scholar
  10. 10.
    Wallinga, J., Teunis, P.: Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. American Journal of Epidemiology 160(6), 509–516 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Bin Yu
    • 1
  • Jijun Wang
    • 1
  • Michael McGowan
    • 1
  • Ganesh Vaidyanathan
    • 1
  • Kristofer Younger
    • 1
  1. 1.Quantum Leap InnovationsNewarkUSA

Personalised recommendations