The Mechanism and Phenomena of Adaptive Human Behavior During an Epidemic and the Role of Information



Disease transmission can be described phenomenologically at the population level or mechanistically as the aggregate result of individual behaviors. To explain why epidemics evolve as they do in response to information, a mechanistic approach is required. However, taking a mechanistic approach reveals that information can be parsed in terms of forecasting models or the approach to forming expectations, timeliness or quality of information, and information processing and how the information is used to make trade-offs. We develop a mechanistic model that uses microeconomic theory to describe adaptive or strategic human behavior. We show that phenomenological forecasting models and forecasting models based on classical epidemiological theory guide human behavior towards similar biological results, but different social well-being results. Moreover, we find that assumptions about information processing method, i.e., the utility function of individuals, may have a substantial influence on an epidemic.


Migration Transportation Nash Wolfram 



This research was made possible by grant 1R01GM100471-01 from the National Institute of General Medical Sciences (NIGMS) at the National Institutes of Health, and by grants from the National Science Foundation (NSF - Grant DMPS-0838705), the National Security Agency (NSA - Grant H98230-09-1-0104), and support from the Office of the Provost of Arizona State University.


  1. 1.
    Almond, D.: J. Polit. Econ. 114, 672–712 (2006)CrossRefGoogle Scholar
  2. 2.
    Almond, D., Mazumder, B.: Am. Econ. Rev. 95, 258–262 (2005)CrossRefGoogle Scholar
  3. 3.
    Anderson, R.M., May, R.M.: Nature 280, 361-367 (1979)CrossRefGoogle Scholar
  4. 4.
    Aparicio, J.P., Pascual, M.: Proceedings of the Royal Society, London B. Vol. 274, pp. 505–512 (2007)Google Scholar
  5. 5.
    Auld, M.C.: J. Health Econ. 22, 361-377 (2003)CrossRefGoogle Scholar
  6. 6.
    Begon, M., Bennett, M., Bowers, R.G., French, N.P., Hazel, S.M., Turner, J.: Epidemiol. Infect. 129, 147–153 (2002)CrossRefGoogle Scholar
  7. 7.
    Boulier, B.L., Satta, T.S., Goldfarb, R.S.: The B.E. J. Econ. Anal. Policy 7 (2007)Google Scholar
  8. 8.
    Caley, P., Philips, D.J., Mccracken, K.: J. Roy. Soc. Interface 5, 631–639 (2007)CrossRefGoogle Scholar
  9. 9.
    Capasso, V., Serio, G.: Math. Biosci. 42, 43–61 (1978)MathSciNetMATHCrossRefGoogle Scholar
  10. 10.
    Chakraborty, S., Papageorgiou, C., Sebastian, F.P.: J. Monetary Econ. 57, 859–872 (2010)CrossRefGoogle Scholar
  11. 11.
    Chen, F., Jiang, M., Rabidoux, S., Tobinson, S.: J. Theor. Biol. 278, 107–119 (2011)CrossRefGoogle Scholar
  12. 12.
    Chen, F.H.: Math. Biosci. 217, 125–133 (2009)MathSciNetMATHCrossRefGoogle Scholar
  13. 13.
    Chow, G.C.: The Rev. Econ. Stat. 71, 376–384 (1989)CrossRefGoogle Scholar
  14. 14.
    Chowell, G., Bertozzi, S.M., Colchero, M.A., Alpuche-Aranda, C., Hernandez, M., Miller, M.A.: The New Engl. J. Med. 361, 674–679 (2009)CrossRefGoogle Scholar
  15. 15.
    Chowell, G., Brauer, F.: The basic reproduction number of infectious diseases: Computation and estimation using compartmental epidemic models. In: Chowell, G., Hyman, J.M., Bettencourt, L.M.A., Castillo-Chavez, C. (eds.) Mathematical and Statistical Estimation Approaches in Epidemiology. Springer, New York (2009)CrossRefGoogle Scholar
  16. 16.
    Cohen, J.E., Enserink, M.: Science 324, 572–573 (2009)CrossRefGoogle Scholar
  17. 17.
    Cui, J., Sun, Y., Zhu, H.: J. Dyn. Diff. Equat. 20, 31–53 (2008)MathSciNetMATHCrossRefGoogle Scholar
  18. 18.
    d’Onofrio, A., Manfredi, P.: J. Theor. Biol. 256, 473-478 (2009)Google Scholar
  19. 19.
    Diekmann, O., Heesterbeek, J.A.P., Metz, J.A.J.: J. Math. Biol. 28, 365–382 (1990)MathSciNetMATHCrossRefGoogle Scholar
  20. 20.
    Fenichel, E.P., Castillo-Chaves, C., Ceddia, M.G., Chowell, G., Gonzalez Parra, P.A., Hickling, G.J., Holloway, G., Horan, R., Morin, B., Perrings, C., Springborn, M., Velazquez, L., Villalobos, C.: Proceedings of the National Academy of Sciences. Vol. 108, pp. 6306–6311 (2011)Google Scholar
  21. 21.
    Francis, P.J.: Dynamic epidemiology and the market for vaccinations. J. Public Econ. 63, 383–406 (1997)CrossRefGoogle Scholar
  22. 22.
    Francis, P.J.: J. Econ. Dyn. Control 28, 2037–2054 (2004)MathSciNetMATHCrossRefGoogle Scholar
  23. 23.
    Funk, S., Salathe, M., Jansen, V.A.A.: J. Roy. Soc. Interface 7, 1247–1256 (2010)CrossRefGoogle Scholar
  24. 24.
    Galvani, A.P., Reluga, T.C., Chapman, G.B.: Proceedings of the National Academy of Sciences. Vol. 104, pp. 5692–5697 (2007)Google Scholar
  25. 25.
    Geoffard, P.Y., Philipson, T.: Int. Econ. Rev. 37, 603–624 (1996)MATHCrossRefGoogle Scholar
  26. 26.
    Geoffard, P., Philipson, T.: Biometrika 82, 101–111 (1995)MathSciNetMATHCrossRefGoogle Scholar
  27. 27.
    Glass, R.J., Glass, L.M., Beyeler, W.E., Min, H.J.: Emerg. Infect. Dis. 12, 1671–1681 (2006)CrossRefGoogle Scholar
  28. 28.
    Heesterbeek, J.A.P., Roberts, M.G.: Math. Biosci. 206, 3–10 (2007)MathSciNetMATHCrossRefGoogle Scholar
  29. 29.
    Heffernan, J.M., Smith, R.J., Wahl, L.M.: J. Roy. Soc. Interface 2, 281–293 (2005)CrossRefGoogle Scholar
  30. 30.
    Hethcote, H.W.: SIAM Rev. 42, 599–653 (2000)MathSciNetMATHCrossRefGoogle Scholar
  31. 31.
    Keeling, M.J., Grenfell, B.T.: J. Theor. Biol. 203, 51–61 (2000)CrossRefGoogle Scholar
  32. 32.
    Keogh-Brown, M.R., Wren-Lewis, S., Edmunds, W.J., Beutels, P., Smith, R.D.: Health Econ. 19, 1345–1360 (2010)CrossRefGoogle Scholar
  33. 33.
    Kermack, W.O., McKendrick, A.G.: Proceedings of the Royal Society, London Series A. Vol. 115, pp. 700–721 (1929)CrossRefGoogle Scholar
  34. 34.
    Korobeinikov, A., Maini, P.K.: Math. Med. Biol. 22, 113–128 (2005)MATHCrossRefGoogle Scholar
  35. 35.
    Kremer, M.: The Q. J. Econ. 111, 549–573 (1996)MATHCrossRefGoogle Scholar
  36. 36.
    Longini, I.M., Halloran, M.E., Nizam, A., Yang, Y.: Am. J. Epidemiol. 169, 623–633 (2004)CrossRefGoogle Scholar
  37. 37.
    Marcet, A., Sargent, T.J.: Am. Econ. Rev. 78, 168–172 (1988)Google Scholar
  38. 38.
    Mccallum, H.I., Barlow, N., Hone, J.: TRENDS Ecol. Evol. 16, 295–300 (2001)CrossRefGoogle Scholar
  39. 39.
    Mesnard, A., Seabright, P.: J. Public Econ. 93, 931–938 (2009)CrossRefGoogle Scholar
  40. 40.
    Mitchell, M.: Complexity: A Guided Tour. Oxford University Press, New York (2009)MATHGoogle Scholar
  41. 41.
    Nishiura, H., Castillo-Chaves, C., Safan, M., Chowell, G.: Eurosurveillance 14, 1–4 (2009)Google Scholar
  42. 42.
    Philipson, T.: Econ. Epidemiol. Infect. Dis. 1, 1761–1799 (2000)Google Scholar
  43. 43.
    Reluga, T.C.: PLoS Comput. Biol. 6, e1000793 (2010)MathSciNetCrossRefGoogle Scholar
  44. 44.
    Roberts, M.G.: J. Roy. Soc. Interface. 4, 949–961 (2007)CrossRefGoogle Scholar
  45. 45.
    Shaw, L., Schwartz, I.B.: Enhanced vaccine control of epidemics in adaptive networks. Phys. Rev. E 81, 046120 (2010)CrossRefGoogle Scholar
  46. 46.
    Shogren, J.F., Crocker, T.D.: J. Environ. Econ. Manag. 37, 44–51 (1999)MATHCrossRefGoogle Scholar
  47. 47.
    Smith, R.D., Keogh-Brown, M.R., Barnett, T., Tait, J.: BMJ 339, b4571 (2009)CrossRefGoogle Scholar
  48. 48.
    Varian, H.R.: Microeconomic Analysis 3rd. W.W. Norton and Company, New York (1992)Google Scholar
  49. 49.
    Veliov, V.M.: J. Math. Biol. 51, 123–143 (2005)MathSciNetMATHCrossRefGoogle Scholar
  50. 50.
    World Health Organization. Emerg. Infect. Dis. 12, 88–94 (2006)Google Scholar

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© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.School of Forestry and Environmental StudiesYale UniversityNew HavenUSA
  2. 2.Mathematical, Computational and Modeling Sciences CenterArizona State UniversityTempeUSA

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