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Deriving Behavior Model Parameters from Survey Data: Self‐Protective Behavior Adoption During the 2009–2010 Influenza A(H1N1) Pandemic

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  • David P. Durham
  • Elizabeth A. Casman
  • Steven M. Albert

Abstract

In this paper, we demonstrate how public opinion surveys can be designed to collect information pertinent to computational behavior modeling, and we present the results of a public opinion and behavior survey conducted during the 2009–2010 H1N1 influenza pandemic. The results are used to parameterize the Health Belief Model of individual health‐protective decision making. Survey subjects were asked questions about their perceptions of the then‐circulating influenza and attitudes towards two personal protective behaviors: vaccination and avoidance of crowds. We empirically address two important issues in applying the Health Belief Model of behavior to computational infectious disease simulation: (1) the factors dynamically influencing the states of the Health Belief Model variables and (2) the appropriateness of the Health Belief Model in describing self‐protective behavior in the context of pandemic influenza.

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  • David P. Durham & Elizabeth A. Casman & Steven M. Albert, 2012. "Deriving Behavior Model Parameters from Survey Data: Self‐Protective Behavior Adoption During the 2009–2010 Influenza A(H1N1) Pandemic," Risk Analysis, John Wiley & Sons, vol. 32(12), pages 2020-2031, December.
  • Handle: RePEc:wly:riskan:v:32:y:2012:i:12:p:2020-2031
    DOI: 10.1111/j.1539-6924.2012.01823.x
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    References listed on IDEAS

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    1. Tang, C.S.K. & Wong, C.-Y., 2003. "An Outbreak of the Severe Acute Respiratory Syndrome: Predictors of Health Behaviors and Effect of Community Prevention Measures in Hong Kong, China," American Journal of Public Health, American Public Health Association, vol. 93(11), pages 1887-1889.
    2. Anders A F Wahlberg & Lennart Sjoberg, 2000. "Risk perception and the media," Journal of Risk Research, Taylor & Francis Journals, vol. 3(1), pages 31-50, January.
    3. Jiuchang Wei & Dingtao Zhao & Liang Liang, 2009. "Estimating the growth models of news stories on disasters," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(9), pages 1741-1755, September.
    4. Loewenstein, George & Mather, Jane, 1990. "Dynamic Processes in Risk Perception," Journal of Risk and Uncertainty, Springer, vol. 3(2), pages 155-175, June.
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    1. Wang, Fei & Yuan, Yu & Lu, Liangdong, 2021. "Dynamical prediction model of consumers’ purchase intentions regarding anti-smog products during smog risk: Taking the information flow perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    2. Ann Pearman & MacKenzie L. Hughes & Clara W. Coblenz & Emily L. Smith & Shevaun D. Neupert, 2023. "A Precautionary Tale: Individual Decision Making in the Time of COVID-19," IJERPH, MDPI, vol. 20(5), pages 1-14, March.
    3. Sunhee Kim & Seoyong Kim, 2020. "Analysis of the Impact of Health Beliefs and Resource Factors on Preventive Behaviors against the COVID-19 Pandemic," IJERPH, MDPI, vol. 17(22), pages 1-21, November.
    4. Sudhir Venkatesan & Jonathan S Nguyen-Van-Tam & Peer-Olaf Siebers, 2019. "A novel framework for evaluating the impact of individual decision-making on public health outcomes and its potential application to study antiviral treatment collection during an influenza pandemic," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-14, October.
    5. d’Onofrio, Alberto & Banerjee, Malay & Manfredi, Piero, 2020. "Spatial behavioural responses to the spread of an infectious disease can suppress Turing and Turing–Hopf patterning of the disease," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).

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