Coevolution of Epidemics, Social Networks, and Individual Behavior: A Case Study

  • Jiangzhuo Chen
  • Achla Marathe
  • Madhav Marathe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6007)


This research shows how a limited supply of antivirals can be distributed optimally between the hospitals and the market so that the attack rate is minimized and enough revenue is generated to recover the cost of the antivirals. Results using an individual based model find that prevalence elastic demand behavior delays the epidemic and change in the social contact network induced by isolation reduces the peak of the epidemic significantly. A microeconomic analysis methodology combining behavioral economics and agent-based simulation is a major contribution of this work. In this paper we apply this methodology to analyze the fairness of the stockpile distribution, and the response of human behavior to disease prevalence level and its interaction with the market.


social network epidemic antiviral behavioral economics microeconomic analysis 


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  1. 1.
    Centre approves restricted retail sale of Tamiflu (2009),
  2. 2.
    Bailey, N.T.: The mathematical theory of infectious diseases and its applications. Hafner Press, New York (1975)MATHGoogle Scholar
  3. 3.
    Barrett, C., Bisset, K., Leidig, J., Marathe, A., Marathe, M.: Estimating the impact of public and private strategies for controlling an epidemic: A multi-agent approach. In: Proceedings of the 21st IAAI Conference (2009)Google Scholar
  4. 4.
    Beckman, R.J., Baggerly, K.A., McKay, M.D.: Creating synthetic baseline populations. Transportation Research, Part A: Policy and Practice 30, 415–429 (1996)CrossRefGoogle Scholar
  5. 5.
    Bisset, K., Chen, J., Feng, X., Kumar, V.A., Marathe, M.: EpiFast: a fast algorithm for large scale realistic epidemic simulations on distributed memory systems. In: Proceedings of the 23rd International Conference on Supercomputing (ICS), pp. 430–439 (2009)Google Scholar
  6. 6.
    Bisset, K., Marathe, M.: A cyber environment to support pandemic planning and response. DOE SciDAC Review Magazine (13) (Summer 2009)Google Scholar
  7. 7.
    Dept. of Health and Huamn Services. Guidance on antiviral drug use during an influenza pandemic (2009), (accessed on November 6, 2009)
  8. 8.
    Dept. of Health and Huamn Services. HHS pandemic influenza plan (2007)Google Scholar
  9. 9.
    Epstein, J., Eubank, S., Lipsitch, M., Hammond, R., Bergstrom, C., Goldstein, E., Marathe, A., Raifman, M., Lewis, B.: Modeling of distribution alternatives of home antiviral drug stockpiling. In: NIH MIDAS Meeting (June 17, 2008)Google Scholar
  10. 10.
    Eubank, S., Guclu, H., Kumar, V.A., Marathe, M., Srinivasan, A., Toroczkai, Z., Wang, N.: Modeling disease outbreaks in realistic urban social networks. Nature 429, 180–184 (2004)CrossRefGoogle Scholar
  11. 11.
    Ferguson, N.L., Cummings, D.A.T., Cauchemez, S., et al.: Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature 437, 209–214 (2005)CrossRefGoogle Scholar
  12. 12.
    Ferguson, N.L., Cummings, D.A.T., Fraser, C., Cajka, J.C., Cooley, P.C., Burke, D.S.: Strategies for mitigating an influenza pandemic. Nature 442, 448–452 (2006)CrossRefGoogle Scholar
  13. 13.
    Germann, T., Kadau, K., Longini Jr, I.M., Macken, C.A.: Mitigation strategies for pandemic influenza in the United States. PNAS 103(15), 5935–5940 (2006)CrossRefGoogle Scholar
  14. 14.
    Gersovitz, M., Hammer, J.S.: Infectious diseases, public policy, and the marriage of economics and epidemiology. The World Bank Research Observer 18(2), 129–157 (2003)CrossRefGoogle Scholar
  15. 15.
    Kremer, M.: Integrating behavioral choice into epidemiological models of the AIDS epidemic. The Quarterly Journal Of Economics 111(2), 549–573 (1996)MATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Kuznetsov, Y., Piccardi, C.: Bifurcation analysis of periodic SEIR and SIR epidemic models. Journal of Mathematical Biology 32, 109–121 (1994)MATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Meyers, L., Newman, M., Martin, M., Schrag, S.: Applying network theory to epidemics: Control measures for outbreaks of mycoplasma pneumonia. Emerging Infectious Diseases 9, 204–210 (2003)Google Scholar
  18. 18.
    Monto, A., Pichichero, M., Blanckenberg, S., et al.: Zanamivir prophylaxis: an effective strategy for the prevention of influenza types A and B within households. J. Infect Dis. 186, 1582–1588 (2002)CrossRefGoogle Scholar
  19. 19.
    Murray, J.D.: Mathematical Biology: I. An Introduction, 3rd edn. Springer, Heidelberg (2007)Google Scholar
  20. 20.
    Philipson, T.: Economic epidemiology and infectious diseases. In: Culyer, A.J., Newhouse, J.P. (eds.) Handbook of Health Economics, vol. 1, ch.33, pp. 1761–1799. Elsevier, Amsterdam (2000)Google Scholar
  21. 21.
    Rabin, M.: A perspective on psychology and economics. European Economic Review. 46, 657–685 (2002)CrossRefGoogle Scholar
  22. 22.
    Welliver, R., Monto, A., Carewicz, O., et al.: Effectiveness of oseltamivir in preventing influenza in household contacts: a randomized controlled trial. JAMA 285, 748–774 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jiangzhuo Chen
    • 1
  • Achla Marathe
    • 1
    • 2
  • Madhav Marathe
    • 1
    • 3
  1. 1.Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics InstituteVirginia TechBlacksburgUSA
  2. 2.Department of Agricultural and Applied EconomicsVirginia TechBlacksburgUSA
  3. 3.Department of Computer ScienceVirginia TechBlacksburgUSA

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