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Proc Natl Acad Sci USA. Adaptive human behavior in epidemiological models
Adaptive human behavior in epidemiological models (PNAS, abstract, edited)
[Source: Proc Natl Acad Sci USA, full text: <cite cite="http://www.pnas.org/content/108/15/6306.short?rss=1">Adaptive human behavior in epidemiological models</cite>. Abstract, edited.]
Adaptive human behavior in epidemiological models
1. Eli P. Fenichel a,1, 2. Carlos Castillo-Chavez b, 3. M. G. Ceddia c, 4. Gerardo Chowell b,d, 5. Paula A. Gonzalez Parra e, 6. Graham J. Hickling f, 7. Garth Holloway c, 8. Richard Horan g, 9. Benjamin Morin b, 10. Charles Perrings a, 11. Michael Springborn h, 12. Leticia Velazquez e, and 13. Cristina Villalobos i
Author Affiliations
1. a School of Life Sciences and ecoSERVICES Group, Arizona State University, Tempe, AZ 85287-4501;
2. b School of Human Evolution and Social Change, Arizona State University, Tempe, AZ 85287;
3. c Department of Food Economics and Marketing, School of Agriculture Policy and Development, University of Reading, RG6 6AR Reading, United Kingdom;
4. d Division of Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD 20892-2220;
5. e Program in Computational Science, University of Texas at El Paso, El Paso, TX 79968-0514;
6. f Center 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. g Department of Agricultural, Food, and Resource Economics, Michigan State University, East Lansing, MI 48824;
8. h Department of Environmental Science and Policy, University of California, Davis, CA 95616; and
9. i Department 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.
* susceptible?infected?recovered model
* R0
* reproductive number
* bioeconomics
Footnotes
* ↵1 To whom correspondence should be addressed. E-mail: Eli.Fenichel@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 https://www.pnas.org/lookup/suppl/do...DCSupplemental.
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[Source: Proc Natl Acad Sci USA, full text: <cite cite="http://www.pnas.org/content/108/15/6306.short?rss=1">Adaptive human behavior in epidemiological models</cite>. Abstract, edited.]
Adaptive human behavior in epidemiological models
1. Eli P. Fenichel a,1, 2. Carlos Castillo-Chavez b, 3. M. G. Ceddia c, 4. Gerardo Chowell b,d, 5. Paula A. Gonzalez Parra e, 6. Graham J. Hickling f, 7. Garth Holloway c, 8. Richard Horan g, 9. Benjamin Morin b, 10. Charles Perrings a, 11. Michael Springborn h, 12. Leticia Velazquez e, and 13. Cristina Villalobos i
Author Affiliations
1. a School of Life Sciences and ecoSERVICES Group, Arizona State University, Tempe, AZ 85287-4501;
2. b School of Human Evolution and Social Change, Arizona State University, Tempe, AZ 85287;
3. c Department of Food Economics and Marketing, School of Agriculture Policy and Development, University of Reading, RG6 6AR Reading, United Kingdom;
4. d Division of Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD 20892-2220;
5. e Program in Computational Science, University of Texas at El Paso, El Paso, TX 79968-0514;
6. f Center 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. g Department of Agricultural, Food, and Resource Economics, Michigan State University, East Lansing, MI 48824;
8. h Department of Environmental Science and Policy, University of California, Davis, CA 95616; and
9. i Department 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.
* susceptible?infected?recovered model
* R0
* reproductive number
* bioeconomics
Footnotes
* ↵1 To whom correspondence should be addressed. E-mail: Eli.Fenichel@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 https://www.pnas.org/lookup/suppl/do...DCSupplemental.
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