Deriving behavior model parameters from survey data: Self-protective behavior adoption during the 2009–2010 Influenza A(H1N1) pandemic
Identifieur interne : 000857 ( Pmc/Corpus ); précédent : 000856; suivant : 000858Deriving behavior model parameters from survey data: Self-protective behavior adoption during the 2009–2010 Influenza A(H1N1) pandemic
Auteurs : David P. Durham ; Elizabeth A. Casman ; Steven M. AlbertSource :
- Risk analysis : an official publication of the Society for Risk Analysis [ 0272-4332 ] ; 2012.
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.
Url:
DOI: 10.1111/j.1539-6924.2012.01823.x
PubMed: 22563796
PubMed Central: 3755610
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PMC:3755610Le document en format XML
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<author><name sortKey="Durham, David P" sort="Durham, David P" uniqKey="Durham D" first="David P." last="Durham">David P. Durham</name>
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<affiliation><nlm:aff id="A2">Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA</nlm:aff>
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<author><name sortKey="Casman, Elizabeth A" sort="Casman, Elizabeth A" uniqKey="Casman E" first="Elizabeth A." last="Casman">Elizabeth A. Casman</name>
<affiliation><nlm:aff id="A2">Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA</nlm:aff>
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<author><name sortKey="Albert, Steven M" sort="Albert, Steven M" uniqKey="Albert S" first="Steven M." last="Albert">Steven M. Albert</name>
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<front><div type="abstract" xml:lang="en"><p id="P1">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.</p>
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<journal-id journal-id-type="nlm-ta">Risk Anal</journal-id>
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<article-categories><subj-group subj-group-type="heading"><subject>Article</subject>
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<title-group><article-title>Deriving behavior model parameters from survey data: Self-protective behavior adoption during the 2009–2010 Influenza A(H1N1) pandemic</article-title>
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<contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Durham</surname>
<given-names>David P.</given-names>
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<email>ddurham@alumni.cmu.edu</email>
<xref ref-type="aff" rid="A1">1</xref>
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<contrib contrib-type="author"><name><surname>Casman</surname>
<given-names>Elizabeth A.</given-names>
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<email>casman@andrew.cmu.edu</email>
<xref ref-type="aff" rid="A2">2</xref>
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<contrib contrib-type="author"><name><surname>Albert</surname>
<given-names>Steven M.</given-names>
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<email>smalbert@pitt.edu</email>
<xref ref-type="aff" rid="A3">3</xref>
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<aff id="A1"><label>1</label>
School of Public Health, Yale University, New Haven, CT, USA</aff>
<aff id="A2"><label>2</label>
Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA</aff>
<aff id="A3"><label>3</label>
Department of Behavioral and Community Health Sciences, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA</aff>
<pub-date pub-type="nihms-submitted"><day>25</day>
<month>7</month>
<year>2013</year>
</pub-date>
<pub-date pub-type="epub"><day>07</day>
<month>5</month>
<year>2012</year>
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<pub-date pub-type="ppub"><month>12</month>
<year>2012</year>
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<pub-date pub-type="pmc-release"><day>28</day>
<month>8</month>
<year>2013</year>
</pub-date>
<volume>32</volume>
<issue>12</issue>
<fpage>2020</fpage>
<lpage>2031</lpage>
<abstract><p id="P1">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.</p>
</abstract>
<kwd-group><kwd>Health Belief Model</kwd>
<kwd>Mathematical modeling</kwd>
<kwd>H1N1</kwd>
<kwd>opinion survey</kwd>
<kwd>behavior change</kwd>
</kwd-group>
<funding-group><award-group><funding-source country="United States">National Institute of General Medical Sciences : NIGMS</funding-source>
<award-id>U54 GM088491 || GM</award-id>
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