%0 Journal Article %1 Fenichela20116306 %A Fenichela, E.P. %A Castillo-Chavezb, C. %A Ceddiac, M.G. %A Chowellb, G. %A Gonzalez Parrae, P.A. %A Hickling, G.J. %A Holloway, G. %A Horan, R. %A Morin, B. %A Perrings, C. %A Springborn, M. %A Velazquez, L. %A Villalobos, C. %D 2011 %J Proceedings of the National Academy of Sciences of the United States of America %K Adaptation, Behavior; Communicable Diseases; Economic; Humans; Models, Psychological Psychological; adaptive aspect; behavior; conference decision epidemic; epidemiology; human journal; making; model, paper; prediction; priority relation; social statistical %N 15 %P 6306-6311 %R http://dx.doi.org/10.1073/pnas.1011250108 %T Adaptive human behavior in epidemiological models %U http://dx.doi.org/10.1073/pnas.1011250108 %V 108 %X 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 epidemiologicalmodels. 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-toperson 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.

@article{Fenichela20116306, 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 epidemiologicalmodels. 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-toperson 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.}, added-at = {2017-11-10T22:48:29.000+0100}, affiliation = {School of Life Sciences and ecoSERVICES Group, Arizona State University, Tempe, AZ 85287-4501, United States; School of Human Evolution and Social Change, Arizona State University, Tempe, AZ 85287, United States; Department of Food Economics and Marketing, School of Agriculture Policy and Development, University of Reading, RG6 6AR Reading, United Kingdom; Division of Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD 20892-2220, United States; Program in Computational Science, University of Texas at El Paso, El Paso, TX 79968-0514, United States; Department of Forestry, Wildlife, and Fisheries, National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, TN 37996-4563, United States; Department of Agricultural, Food, and Resource Economics, Michigan State University, East Lansing, MI 48824, United States; Department of Environmental Science and Policy, University of California, Davis, CA 95616, United States; Department of Mathematics, University of Texas-Pan American, Edinburg, TX 78539, United States}, author = {Fenichela, E.P. and Castillo-Chavezb, C. and Ceddiac, M.G. and Chowellb, G. and Gonzalez Parrae, P.A. and Hickling, G.J. and Holloway, G. and Horan, R. and Morin, B. and Perrings, C. and Springborn, M. and Velazquez, L. and Villalobos, C.}, author_keywords = {Bioeconomics; R0; Reproductive number; Susceptible-infected-recovered model}, biburl = {https://www.bibsonomy.org/bibtex/28d7ac775f7a7c36adb66e5e2b21d40d0/ccchavez}, coden = {PNASA}, correspondence_address1 = {Fenichela, E. P.; School of Life Sciences and ecoSERVICES Group, Arizona State University, Tempe, AZ 85287-4501, United States; email: Eli.Fenichel@asu.edu}, date-added = {2017-11-10 21:45:26 +0000}, date-modified = {2017-11-10 21:45:26 +0000}, document_type = {Conference Paper}, doi = {http://dx.doi.org/10.1073/pnas.1011250108}, interhash = {9090710bfdbcd580e691131bc79b0f96}, intrahash = {8d7ac775f7a7c36adb66e5e2b21d40d0}, issn = {00278424}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, keywords = {Adaptation, Behavior; Communicable Diseases; Economic; Humans; Models, Psychological Psychological; adaptive aspect; behavior; conference decision epidemic; epidemiology; human journal; making; model, paper; prediction; priority relation; social statistical}, language = {English}, number = 15, pages = {6306-6311}, pubmed_id = {21444809}, timestamp = {2017-11-10T22:48:29.000+0100}, title = {Adaptive human behavior in epidemiological models}, url = {http://dx.doi.org/10.1073/pnas.1011250108}, volume = 108, year = 2011 }