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Adaptive human behavior in epidemiological models

  1. Cristina Villalobosi
  1. aSchool of Life Sciences and ecoSERVICES Group, Arizona State University, Tempe, AZ 85287-4501;
  2. bSchool of Human Evolution and Social Change, Arizona State University, Tempe, AZ 85287;
  3. cDepartment of Food Economics and Marketing, School of Agriculture Policy and Development, University of Reading, RG6 6AR Reading, United Kingdom;
  4. dDivision of Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD 20892-2220;
  5. eProgram in Computational Science, University of Texas at El Paso, El Paso, TX 79968-0514;
  6. fCenter for Wildlife Health, Department of Forestry, Wildlife, and Fisheries, and National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, TN 37996-4563;
  7. gDepartment of Agricultural, Food, and Resource Economics, Michigan State University, East Lansing, MI 48824;
  8. hDepartment of Environmental Science and Policy, University of California, Davis, CA 95616; and
  9. iDepartment of Mathematics, University of Texas–Pan American, Edinburg, TX 78539

  1. Edited by Partha Sarathi Dasgupta, University of Cambridge, Cambridge, United Kingdom, and approved February 23, 2011 (received for review July 30, 2010)

Abstract

The science and management of infectious disease are entering a new stage. Increasingly public policy to manage epidemics focuses on motivating people, through social distancing policies, to alter their behavior to reduce contacts and reduce public disease risk. Person-to-person contacts drive human disease dynamics. People value such contacts and are willing to accept some disease risk to gain contact-related benefits. The cost–benefit trade-offs that shape contact behavior, and hence the course of epidemics, are often only implicitly incorporated in epidemiological models. This approach creates difficulty in parsing out the effects of adaptive behavior. We use an epidemiological–economic model of disease dynamics to explicitly model the trade-offs that drive person-to-person contact decisions. Results indicate that including adaptive human behavior significantly changes the predicted course of epidemics and that this inclusion has implications for parameter estimation and interpretation and for the development of social distancing policies. Acknowledging adaptive behavior requires a shift in thinking about epidemiological processes and parameters.

Footnotes

  • ↵1To whom correspondence should be addressed. E-mail: Eli.Fenichel{at}asu.edu.

  • Author contributions: E.P.F., C.C.-C., M.G.C., G.C., P.A.G.P., G.J.H., G.H., R.H., C.P., M.S., L.V., and C.V. designed research; E.P.F. performed research; E.P.F. led modeling and led the workshop where research was designed; M.S. contributed to modeling; and E.P.F., M.G.C., G.C., P.A.G.P., G.H., R.H., B.M., C.P., M.S., L.V., and C.V. wrote the paper.

  • The authors declare no conflict of interest.

  • This article is a PNAS Direct Submission.

  • This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1011250108/-/DCSupplemental.

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