A belief-based model for characterizing the spread of awareness and its impacts on individuals' vaccination decisions
Identifieur interne : 000A35 ( Istex/Corpus ); précédent : 000A34; suivant : 000A36A belief-based model for characterizing the spread of awareness and its impacts on individuals' vaccination decisions
Auteurs : Shang Xia ; Jiming LiuSource :
- Journal of the Royal Society Interface [ 1742-5689 ] ; 2014.
Abstract
During an epidemic, individuals' decisions on whether or not to take vaccine may affect the dynamics of disease spread and, therefore, the effectiveness of disease control. Empirical studies have shown that such decisions can be subjected to individuals' awareness about disease and vaccine, such as their perceived disease severity and vaccine safety. The aim of this paper is to gain a better understanding of individuals' vaccination behaviour by modelling the spread of awareness in a group of socially connected individuals and examining the associated impacts on their vaccination decision-making. In our model, we examine whether or not individuals will get vaccinated as well as when they would. In doing so, we consider three possible decisions from an individual, i.e. to accept, to reject, and yet to decide, and further associate them with a set of belief values. Next, we extend the Dempster–Shafer theory to characterize individuals' belief value updates and their decision-making, having incorporated the awareness obtained from their connected neighbours. Furthermore, we examine two factors that will affect individuals' vaccination decisions: (i) reporting rates of disease- and vaccine-related events, and (ii) fading coefficient of awareness spread. By doing so, we can assess the impacts of awareness spread by evaluating the vaccination dynamics in terms of the number of vaccinated individuals. The results have demonstrated that the former influences the ratio of vaccinated individuals, whereas the latter affects the time when individuals decide to take vaccine.
Url:
DOI: 10.1098/rsif.2014.0013
Links to Exploration step
ISTEX:E68119CAA8C74DBCAABE30AF45939C204F2E45A2Le document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title>A belief-based model for characterizing the spread of awareness and its impacts on individuals' vaccination decisions</title>
<author><name sortKey="Xia, Shang" sort="Xia, Shang" uniqKey="Xia S" first="Shang" last="Xia">Shang Xia</name>
<affiliation><mods:affiliation>National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, People's Republic of China</mods:affiliation>
</affiliation>
</author>
<author><name sortKey="Liu, Jiming" sort="Liu, Jiming" uniqKey="Liu J" first="Jiming" last="Liu">Jiming Liu</name>
<affiliation><mods:affiliation>Department of Computer Science, Hong Kong Baptist University, Hong Kong</mods:affiliation>
</affiliation>
<affiliation><mods:affiliation>E-mail: jiming@comp.hkbu.edu.hk</mods:affiliation>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">ISTEX</idno>
<idno type="RBID">ISTEX:E68119CAA8C74DBCAABE30AF45939C204F2E45A2</idno>
<date when="2014" year="2014">2014</date>
<idno type="doi">10.1098/rsif.2014.0013</idno>
<idno type="url">https://api.istex.fr/ark:/67375/V84-9XMKW6D1-P/fulltext.pdf</idno>
<idno type="wicri:Area/Istex/Corpus">000A35</idno>
<idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">000A35</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title level="a" type="main">A belief-based model for characterizing the spread of awareness and its impacts on individuals' vaccination decisions</title>
<author><name sortKey="Xia, Shang" sort="Xia, Shang" uniqKey="Xia S" first="Shang" last="Xia">Shang Xia</name>
<affiliation><mods:affiliation>National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, People's Republic of China</mods:affiliation>
</affiliation>
</author>
<author><name sortKey="Liu, Jiming" sort="Liu, Jiming" uniqKey="Liu J" first="Jiming" last="Liu">Jiming Liu</name>
<affiliation><mods:affiliation>Department of Computer Science, Hong Kong Baptist University, Hong Kong</mods:affiliation>
</affiliation>
<affiliation><mods:affiliation>E-mail: jiming@comp.hkbu.edu.hk</mods:affiliation>
</affiliation>
</author>
</analytic>
<monogr></monogr>
<series><title level="j" type="main">Journal of the Royal Society Interface</title>
<title level="j" type="abbrev">J. R. Soc. Interface</title>
<idno type="ISSN">1742-5689</idno>
<idno type="eISSN">1742-5662</idno>
<imprint><publisher>The Royal Society</publisher>
<date type="published">2014</date>
<biblScope unit="vol">11</biblScope>
<biblScope unit="issue">94</biblScope>
</imprint>
<idno type="ISSN">1742-5689</idno>
</series>
</biblStruct>
</sourceDesc>
<seriesStmt><idno type="ISSN">1742-5689</idno>
</seriesStmt>
</fileDesc>
<profileDesc><textClass></textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract">During an epidemic, individuals' decisions on whether or not to take vaccine may affect the dynamics of disease spread and, therefore, the effectiveness of disease control. Empirical studies have shown that such decisions can be subjected to individuals' awareness about disease and vaccine, such as their perceived disease severity and vaccine safety. The aim of this paper is to gain a better understanding of individuals' vaccination behaviour by modelling the spread of awareness in a group of socially connected individuals and examining the associated impacts on their vaccination decision-making. In our model, we examine whether or not individuals will get vaccinated as well as when they would. In doing so, we consider three possible decisions from an individual, i.e. to accept, to reject, and yet to decide, and further associate them with a set of belief values. Next, we extend the Dempster–Shafer theory to characterize individuals' belief value updates and their decision-making, having incorporated the awareness obtained from their connected neighbours. Furthermore, we examine two factors that will affect individuals' vaccination decisions: (i) reporting rates of disease- and vaccine-related events, and (ii) fading coefficient of awareness spread. By doing so, we can assess the impacts of awareness spread by evaluating the vaccination dynamics in terms of the number of vaccinated individuals. The results have demonstrated that the former influences the ratio of vaccinated individuals, whereas the latter affects the time when individuals decide to take vaccine.</div>
</front>
</TEI>
<istex><corpusName>rsl</corpusName>
<author><json:item><name>Shang Xia</name>
<affiliations><json:string>National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, People's Republic of China</json:string>
</affiliations>
</json:item>
<json:item><name>Jiming Liu</name>
<affiliations><json:string>Department of Computer Science, Hong Kong Baptist University, Hong Kong</json:string>
<json:string>E-mail: jiming@comp.hkbu.edu.hk</json:string>
</affiliations>
</json:item>
</author>
<subject><json:item><lang><json:string>eng</json:string>
</lang>
<value>vaccination decision</value>
</json:item>
<json:item><lang><json:string>eng</json:string>
</lang>
<value>awareness</value>
</json:item>
<json:item><lang><json:string>eng</json:string>
</lang>
<value>belief model</value>
</json:item>
</subject>
<articleId><json:string>rsif20140013</json:string>
</articleId>
<arkIstex>ark:/67375/V84-9XMKW6D1-P</arkIstex>
<language><json:string>eng</json:string>
</language>
<originalGenre><json:string>research-article</json:string>
</originalGenre>
<abstract>During an epidemic, individuals' decisions on whether or not to take vaccine may affect the dynamics of disease spread and, therefore, the effectiveness of disease control. Empirical studies have shown that such decisions can be subjected to individuals' awareness about disease and vaccine, such as their perceived disease severity and vaccine safety. The aim of this paper is to gain a better understanding of individuals' vaccination behaviour by modelling the spread of awareness in a group of socially connected individuals and examining the associated impacts on their vaccination decision-making. In our model, we examine whether or not individuals will get vaccinated as well as when they would. In doing so, we consider three possible decisions from an individual, i.e. to accept, to reject, and yet to decide, and further associate them with a set of belief values. Next, we extend the Dempster–Shafer theory to characterize individuals' belief value updates and their decision-making, having incorporated the awareness obtained from their connected neighbours. Furthermore, we examine two factors that will affect individuals' vaccination decisions: (i) reporting rates of disease- and vaccine-related events, and (ii) fading coefficient of awareness spread. By doing so, we can assess the impacts of awareness spread by evaluating the vaccination dynamics in terms of the number of vaccinated individuals. The results have demonstrated that the former influences the ratio of vaccinated individuals, whereas the latter affects the time when individuals decide to take vaccine.</abstract>
<qualityIndicators><score>9.165</score>
<pdfWordCount>7142</pdfWordCount>
<pdfCharCount>46471</pdfCharCount>
<pdfVersion>1.5</pdfVersion>
<pdfPageCount>10</pdfPageCount>
<pdfPageSize>595.276 x 841.89 pts (A4)</pdfPageSize>
<pdfWordsPerPage>714</pdfWordsPerPage>
<pdfText>true</pdfText>
<refBibsNative>true</refBibsNative>
<abstractWordCount>236</abstractWordCount>
<abstractCharCount>1588</abstractCharCount>
<keywordCount>3</keywordCount>
</qualityIndicators>
<title>A belief-based model for characterizing the spread of awareness and its impacts on individuals' vaccination decisions</title>
<genre><json:string>article</json:string>
</genre>
<host><title>Journal of the Royal Society Interface</title>
<language><json:string>unknown</json:string>
</language>
<issn><json:string>1742-5689</json:string>
</issn>
<eissn><json:string>1742-5662</json:string>
</eissn>
<publisherId><json:string>RSIF</json:string>
</publisherId>
<volume>11</volume>
<issue>94</issue>
<genre><json:string>journal</json:string>
</genre>
<subject><json:item><value>1004</value>
</json:item>
<json:item><value>44</value>
</json:item>
</subject>
</host>
<ark><json:string>ark:/67375/V84-9XMKW6D1-P</json:string>
</ark>
<categories><wos><json:string>1 - science</json:string>
<json:string>2 - multidisciplinary sciences</json:string>
</wos>
<scienceMetrix><json:string>1 - general</json:string>
<json:string>2 - general science & technology</json:string>
<json:string>3 - general science & technology</json:string>
</scienceMetrix>
<scopus><json:string>1 - Physical Sciences</json:string>
<json:string>2 - Engineering</json:string>
<json:string>3 - Biomedical Engineering</json:string>
<json:string>1 - Life Sciences</json:string>
<json:string>2 - Biochemistry, Genetics and Molecular Biology</json:string>
<json:string>3 - Biochemistry</json:string>
<json:string>1 - Physical Sciences</json:string>
<json:string>2 - Materials Science</json:string>
<json:string>3 - Biomaterials</json:string>
<json:string>1 - Physical Sciences</json:string>
<json:string>2 - Chemical Engineering</json:string>
<json:string>3 - Bioengineering</json:string>
<json:string>1 - Life Sciences</json:string>
<json:string>2 - Biochemistry, Genetics and Molecular Biology</json:string>
<json:string>3 - Biophysics</json:string>
<json:string>1 - Life Sciences</json:string>
<json:string>2 - Biochemistry, Genetics and Molecular Biology</json:string>
<json:string>3 - Biotechnology</json:string>
</scopus>
<inist><json:string>1 - sciences appliquees, technologies et medecines</json:string>
<json:string>2 - sciences biologiques et medicales</json:string>
<json:string>3 - sciences biologiques fondamentales et appliquees. psychologie</json:string>
</inist>
</categories>
<publicationDate>2014</publicationDate>
<copyrightDate>2014</copyrightDate>
<doi><json:string>10.1098/rsif.2014.0013</json:string>
</doi>
<id>E68119CAA8C74DBCAABE30AF45939C204F2E45A2</id>
<score>1</score>
<fulltext><json:item><extension>pdf</extension>
<original>true</original>
<mimetype>application/pdf</mimetype>
<uri>https://api.istex.fr/ark:/67375/V84-9XMKW6D1-P/fulltext.pdf</uri>
</json:item>
<json:item><extension>zip</extension>
<original>false</original>
<mimetype>application/zip</mimetype>
<uri>https://api.istex.fr/ark:/67375/V84-9XMKW6D1-P/bundle.zip</uri>
</json:item>
<istex:fulltextTEI uri="https://api.istex.fr/ark:/67375/V84-9XMKW6D1-P/fulltext.tei"><teiHeader><fileDesc><titleStmt><title level="a" type="main">A belief-based model for characterizing the spread of awareness and its impacts on individuals' vaccination decisions</title>
</titleStmt>
<publicationStmt><authority>ISTEX</authority>
<publisher>The Royal Society</publisher>
<availability status="free" source="http://creativecommons.org/licenses/by/3.0/"><p>© 2014 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License <ref type="uri">http://creativecommons.org/licenses/by/3.0/</ref>
, which permits unrestricted use, provided the original author and source are credited.</p>
</availability>
<date type="published">2014</date>
<date type="Copyright" when="2014">2014</date>
</publicationStmt>
<notesStmt><note type="content-type" source="research-article" scheme="https://content-type.data.istex.fr/ark:/67375/XTP-1JC4F85T-7">research-article</note>
<note type="publication-type" scheme="https://publication-type.data.istex.fr/ark:/67375/JMC-0GLKJH51-B">journal</note>
</notesStmt>
<sourceDesc><biblStruct type="article"><analytic><title level="a" type="main">A belief-based model for characterizing the spread of awareness and its impacts on individuals' vaccination decisions</title>
<title level="a" type="short">A belief-based model for characterizing the spread of awareness and its impacts on individuals' vaccination decisions</title>
<author xml:id="author-0000"><persName><surname>Xia</surname>
<forename type="first">Shang</forename>
</persName>
<affiliation><orgName type="institution">National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention</orgName>
<address><addrLine>Shanghai</addrLine>
<country key="" xml:lang="en"></country>
</address>
</affiliation>
</author>
<author xml:id="author-0001"><persName><surname>Liu</surname>
<forename type="first">Jiming</forename>
</persName>
<affiliation><orgName type="institution">Hong Kong Baptist University</orgName>
<address><addrLine>Department of Computer Science</addrLine>
<country key="HK" xml:lang="en">HONG KONG</country>
</address>
</affiliation>
</author>
<idno type="istex">E68119CAA8C74DBCAABE30AF45939C204F2E45A2</idno>
<idno type="ark">ark:/67375/V84-9XMKW6D1-P</idno>
<idno type="DOI">10.1098/rsif.2014.0013</idno>
<idno type="publisher-id">rsif20140013</idno>
</analytic>
<monogr><title level="j" type="main">Journal of the Royal Society Interface</title>
<title level="j" type="abbrev">J. R. Soc. Interface</title>
<idno type="publisher-id">RSIF</idno>
<idno type="hwp">royinterface</idno>
<idno type="pISSN">1742-5689</idno>
<idno type="eISSN">1742-5662</idno>
<imprint><publisher>The Royal Society</publisher>
<date type="published">2014</date>
<biblScope unit="vol">11</biblScope>
<biblScope unit="issue">94</biblScope>
</imprint>
</monogr>
</biblStruct>
</sourceDesc>
</fileDesc>
<encodingDesc><schemaRef type="ODD" url="https://xml-schema.delivery.istex.fr/tei-istex.odd"></schemaRef>
<appInfo><application ident="pub2tei" version="1.0.41" when="2020-04-06"><label>pub2TEI-ISTEX</label>
<desc>A set of style sheets for converting XML documents encoded in various scientific publisher formats into a common TEI format.
<ref target="http://www.tei-c.org/">We use TEI</ref>
</desc>
</application>
</appInfo>
</encodingDesc>
<profileDesc><abstract><p>During an epidemic, individuals' decisions on whether or not to take vaccine may affect the dynamics of disease spread and, therefore, the effectiveness of disease control. Empirical studies have shown that such decisions can be subjected to individuals' awareness about disease and vaccine, such as their perceived disease severity and vaccine safety. The aim of this paper is to gain a better understanding of individuals' vaccination behaviour by modelling the spread of awareness in a group of socially connected individuals and examining the associated impacts on their vaccination decision-making. In our model, we examine whether or not individuals will get vaccinated as well as when they would. In doing so, we consider three possible decisions from an individual, i.e. to accept, to reject, and yet to decide, and further associate them with a set of belief values. Next, we extend the Dempster–Shafer theory to characterize individuals' belief value updates and their decision-making, having incorporated the awareness obtained from their connected neighbours. Furthermore, we examine two factors that will affect individuals' vaccination decisions: (i) reporting rates of disease- and vaccine-related events, and (ii) fading coefficient of awareness spread. By doing so, we can assess the impacts of awareness spread by evaluating the vaccination dynamics in terms of the number of vaccinated individuals. The results have demonstrated that the former influences the ratio of vaccinated individuals, whereas the latter affects the time when individuals decide to take vaccine.</p>
</abstract>
<textClass ana="subject"><keywords scheme="hwp-journal-coll"><term>1004</term>
<term>44</term>
</keywords>
<keywords scheme="heading"><term>Research articles</term>
</keywords>
</textClass>
<textClass ana="keyword"><keywords><term>vaccination decision</term>
<term>awareness</term>
<term>belief model</term>
</keywords>
</textClass>
<langUsage><language ident="EN"></language>
</langUsage>
</profileDesc>
<revisionDesc><change when="2020-04-06" who="#istex" xml:id="pub2tei">formatting</change>
</revisionDesc>
</teiHeader>
</istex:fulltextTEI>
<json:item><extension>txt</extension>
<original>false</original>
<mimetype>text/plain</mimetype>
<uri>https://api.istex.fr/ark:/67375/V84-9XMKW6D1-P/fulltext.txt</uri>
</json:item>
</fulltext>
<metadata><istex:metadataXml wicri:clean="corpus rsl not found" wicri:toSee="no header"><istex:xmlDeclaration>version="1.0" encoding="utf-8"</istex:xmlDeclaration>
<istex:docType PUBLIC="-//NLM//DTD Journal Publishing DTD v2.3 20070202//EN" URI="journalpublishing.dtd" name="istex:docType"></istex:docType>
<istex:document><article article-type="research-article"><front><journal-meta><journal-id journal-id-type="publisher-id">RSIF</journal-id>
<journal-id journal-id-type="hwp">royinterface</journal-id>
<journal-title>Journal of the Royal Society Interface</journal-title>
<abbrev-journal-title>J. R. Soc. Interface</abbrev-journal-title>
<issn pub-type="ppub">1742-5689</issn>
<issn pub-type="epub">1742-5662</issn>
<publisher><publisher-name>The Royal Society</publisher-name>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="doi">10.1098/rsif.2014.0013</article-id>
<article-id pub-id-type="publisher-id">rsif20140013</article-id>
<article-categories><subj-group subj-group-type="hwp-journal-coll"><subject>1004</subject>
<subject>44</subject>
</subj-group>
<subj-group subj-group-type="heading"><subject>Research articles</subject>
</subj-group>
</article-categories>
<title-group><article-title>A belief-based model for characterizing the spread of awareness and its impacts on individuals' vaccination decisions</article-title>
<alt-title alt-title-type="short">A belief-based model for characterizing the spread of awareness and its impacts on individuals' vaccination decisions</alt-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Xia</surname>
<given-names>Shang</given-names>
</name>
<xref ref-type="aff" rid="af1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Liu</surname>
<given-names>Jiming</given-names>
</name>
<xref ref-type="aff" rid="af2">2</xref>
<xref ref-type="corresp" rid="cor1"></xref>
</contrib>
</contrib-group>
<aff id="af1"><label>1</label>
<institution>National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention</institution>
, <addr-line>Shanghai</addr-line>
, <country>People's Republic of China</country>
</aff>
<aff id="af2"><label>2</label>
<addr-line>Department of Computer Science</addr-line>
, <institution>Hong Kong Baptist University</institution>
, <country>Hong Kong</country>
</aff>
<author-notes><corresp id="cor1">e-mail: <email>jiming@comp.hkbu.edu.hk</email>
</corresp>
</author-notes>
<pub-date pub-type="ppub"><day>6</day>
<month>5</month>
<year>2014</year>
</pub-date>
<volume>11</volume>
<issue>94</issue>
<elocation-id>20140013</elocation-id>
<history><date date-type="received"><day>6</day>
<month>1</month>
<year>2014</year>
</date>
<date date-type="accepted"><day>13</day>
<month>2</month>
<year>2014</year>
</date>
</history>
<permissions><copyright-statement></copyright-statement>
<copyright-year>2014</copyright-year>
<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/3.0/"><p>© 2014 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/3.0/">http://creativecommons.org/licenses/by/3.0/</ext-link>
, which permits unrestricted use, provided the original author and source are credited.</p>
</license>
</permissions>
<self-uri content-type="pdf" xlink:href="rsif20140013.pdf"></self-uri>
<abstract><p>During an epidemic, individuals' decisions on whether or not to take vaccine may affect the dynamics of disease spread and, therefore, the effectiveness of disease control. Empirical studies have shown that such decisions can be subjected to individuals' awareness about disease and vaccine, such as their perceived disease severity and vaccine safety. The aim of this paper is to gain a better understanding of individuals' vaccination behaviour by modelling the spread of awareness in a group of socially connected individuals and examining the associated impacts on their vaccination decision-making. In our model, we examine whether or not individuals will get vaccinated as well as when they would. In doing so, we consider three possible decisions from an individual, i.e. to accept, to reject, and yet to decide, and further associate them with a set of belief values. Next, we extend the Dempster–Shafer theory to characterize individuals' belief value updates and their decision-making, having incorporated the awareness obtained from their connected neighbours. Furthermore, we examine two factors that will affect individuals' vaccination decisions: (i) reporting rates of disease- and vaccine-related events, and (ii) fading coefficient of awareness spread. By doing so, we can assess the impacts of awareness spread by evaluating the vaccination dynamics in terms of the number of vaccinated individuals. The results have demonstrated that the former influences the ratio of vaccinated individuals, whereas the latter affects the time when individuals decide to take vaccine.</p>
</abstract>
<kwd-group><kwd>vaccination decision</kwd>
<kwd>awareness</kwd>
<kwd>belief model</kwd>
</kwd-group>
<custom-meta-wrap><custom-meta><meta-name>cover-date</meta-name>
<meta-value>May 6, 2014</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body><sec id="s1" sec-type="intro"><label>1.</label>
<title>Introduction</title>
<p>In controlling the spread of infectious diseases, the effectiveness of a vaccination programme depends on the ratio of the vaccinated host population [<xref ref-type="bibr" rid="RSIF20140013C1">1</xref>
–<xref ref-type="bibr" rid="RSIF20140013C4">4</xref>
]. For example, vaccination can prevent disease transmissions when the coverage of a host population is above the critical level of the herd immunity threshold [<xref ref-type="bibr" rid="RSIF20140013C5">5</xref>
,<xref ref-type="bibr" rid="RSIF20140013C6">6</xref>
]. In this context, individuals' vaccination decisions on whether or not to take vaccine can play an important role in achieving adequate and sustained vaccination coverage [<xref ref-type="bibr" rid="RSIF20140013C7">7</xref>
,<xref ref-type="bibr" rid="RSIF20140013C8">8</xref>
].</p>
<p>Previous studies on individuals' vaccination decision-making have typically focused on several determinants with respect to individuals' perceived benefits and risks of vaccination, including vaccine-induced immunization [<xref ref-type="bibr" rid="RSIF20140013C9">9</xref>
–<xref ref-type="bibr" rid="RSIF20140013C11">11</xref>
], the possible adverse events following immunization (AEFI) [<xref ref-type="bibr" rid="RSIF20140013C12">12</xref>
,<xref ref-type="bibr" rid="RSIF20140013C13">13</xref>
] as well as social and financial costs associated with disease infection and vaccination, such as the direct costs of vaccination, the expenses for the treatment of disease infection, and the indirect costs in the form of absence from workplaces or schools [<xref ref-type="bibr" rid="RSIF20140013C14">14</xref>
–<xref ref-type="bibr" rid="RSIF20140013C16">16</xref>
]. In this regard, game-theoretical analysis has been widely used to describe individuals' vaccination decisions by examining their personal optimized pay-offs based on the perceived risks and benefits of vaccination [<xref ref-type="bibr" rid="RSIF20140013C17">17</xref>
–<xref ref-type="bibr" rid="RSIF20140013C19">19</xref>
]. Moreover, some studies have looked into the social and psychological aspects of decision-making (e.g. social learning process [<xref ref-type="bibr" rid="RSIF20140013C20">20</xref>
] and imitation behaviour [<xref ref-type="bibr" rid="RSIF20140013C21">21</xref>
–<xref ref-type="bibr" rid="RSIF20140013C24">24</xref>
]). While others have considered the issues of incomplete information by adding either the potential discrepancy between individuals' perceptions and real situations (e.g. the perceived disease prevalence and the adverse effects of vaccine [<xref ref-type="bibr" rid="RSIF20140013C25">25</xref>
,<xref ref-type="bibr" rid="RSIF20140013C26">26</xref>
]) or different sources of information (e.g. previous disease prevalence or vaccination programmes [<xref ref-type="bibr" rid="RSIF20140013C27">27</xref>
–<xref ref-type="bibr" rid="RSIF20140013C29">29</xref>
]).</p>
<p>These earlier decision-making studies have addressed the problem of whether or not individuals will get vaccinated by taking into account their prior knowledge about the disease, the vaccine and the associated costs. While during a vaccination programme, the time when individuals will make their vaccination decisions is still a concern. Furthermore, the above-mentioned prior knowledge may exist only for the routine vaccination programmes against seasonal infectious diseases (e.g. measles and chickenpox [<xref ref-type="bibr" rid="RSIF20140013C30">30</xref>
]). As for a newly developed vaccine against an emerging infectious disease (e.g. vaccine against the 2009 H1N1 influenza [<xref ref-type="bibr" rid="RSIF20140013C31">31</xref>
]), there is always a lack of such prior knowledge. Empirical studies have shown that individuals' vaccination decisions can be subjected to their perceptions about disease and vaccine rather than to the actual situations, which are continuously affected by the social environment with which they interact [<xref ref-type="bibr" rid="RSIF20140013C32">32</xref>
]. Specifically, individuals who are aware of severe disease infections will tend to seek protection from vaccination. For instance, the impacts of a measles epidemic were observed to increase the uptake of measles vaccines [<xref ref-type="bibr" rid="RSIF20140013C33">33</xref>
]. On the other hand, when realizing the potential risks of vaccine, e.g. vaccine-related AEFI, individuals will reduce their willingness to vaccinate themselves, such as in the case of the MMR vaccine (i.e. vaccine against measles, mumps and rubella) scare in the UK in the 1990s [<xref ref-type="bibr" rid="RSIF20140013C34">34</xref>
,<xref ref-type="bibr" rid="RSIF20140013C35">35</xref>
]. In this regard, individuals' awareness about severe disease infections and vaccine-related AEFI will affect their perceptions about disease severity and vaccine safety, and hence, influence their vaccination decisions. In addition, an individual changing his/her vaccination decision does not require direct self-involvement in the reported events, while being informed through others. That is to say, individuals' perceptions can be prompted through the spread of awareness in a host population [<xref ref-type="bibr" rid="RSIF20140013C36">36</xref>
,<xref ref-type="bibr" rid="RSIF20140013C37">37</xref>
], which can potentially alter individuals' vaccination decisions and, hence, affect the effectiveness of vaccination programmes.</p>
<p>In recent years, the rapid emergence of online social media, such as Facebook [<xref ref-type="bibr" rid="RSIF20140013C38">38</xref>
], Twitter [<xref ref-type="bibr" rid="RSIF20140013C39">39</xref>
] and YouTube [<xref ref-type="bibr" rid="RSIF20140013C40">40</xref>
,<xref ref-type="bibr" rid="RSIF20140013C41">41</xref>
], have provided new ways for the spread of public-health-related information [<xref ref-type="bibr" rid="RSIF20140013C42">42</xref>
]. As for vaccination, the online social communities would debate on the efficacy of vaccines [<xref ref-type="bibr" rid="RSIF20140013C43">43</xref>
]; the vaccine-related AEFI would be reported and shared on the Internet [<xref ref-type="bibr" rid="RSIF20140013C44">44</xref>
], and opinions either for or against vaccination would be transmitted from person to person [<xref ref-type="bibr" rid="RSIF20140013C37">37</xref>
]. In this situation, the spread of awareness about disease and vaccine could immediately affect individuals' responses. Therefore, the dynamics of individuals' vaccination decisions will be tightly coupled with that of disease transmissions. The interplay between these two dynamical processes could have a significant consequence on the resulting vaccination coverage for infectious disease control.</p>
<p>In this study, we are interested in studying individuals' vaccination decisions as affected by the spread of awareness about disease and vaccine-related events during an influenza-like epidemic. As illustrated in <xref ref-type="fig" rid="RSIF20140013F1">figure 1</xref>
, we consider a group of individuals that can decide whether or not to take vaccine based on their perceived disease severity and vaccine safety. Specifically, individuals can interact with each other through their social relationships (e.g. friendships on the Facebook and follower relationships on the Twitter). In such a structured host population (i.e. represented by a social network), the awareness about disease and vaccine can spread from person to person and will substantially affect individuals' perceptions about disease severity and vaccine safety. On the one hand, the reported cases of severe disease infections will enhance individuals' perceived disease severity and, hence, increase their tendency to vaccination. On the other hand, the reported events of vaccine-related AEFI will weaken the public confidence on vaccine safety, which will lead to the declined acceptance of vaccination.
<fig id="RSIF20140013F1"><label>Figure 1.</label>
<caption><p>A schematic of the impacts of the spread of awareness on individuals' vaccination decision-making. We consider a group of individuals whose vaccination decisions (i.e. whether or not to take vaccine) depend on their perceptions about disease severity and vaccine safety. We use a social network to characterize the structure of individuals' interactions. The awareness about severe disease infections and vaccine-related AEFI can spread from person to person through their interaction relationships, which will substantially affect their perceived disease severity and vaccine safety and, thus, change their vaccination decisions accordingly. (Online version in colour.)</p>
</caption>
<graphic xlink:href="rsif20140013f01"></graphic>
</fig>
</p>
<p>In this situation, we develop a novel modelling framework for characterizing individuals' vaccination decisions, in which we suppose that an individual will voluntarily decide to accept or reject vaccine based on his/her beliefs on whether or not vaccination is acceptable. In order to examine the time when individuals will make their vaccination decisions, we further assume that if the individual does not have enough confidence for or against vaccination, he/she will not make any firm decision but wait and see the future development. The situation that an individual makes no firm decision may be considered as a state of ‘yet to decide’ owing to uncertainty. In this regard, we introduce three belief variables in the form of <italic>yes</italic>
, <italic>no</italic>
and <italic>no decision</italic>
to characterize the possible decision responses from an individual that he/she will accept or reject vaccine, or has not yet decided, respectively. Owing to the spread of awareness, the individual will update his/her beliefs about vaccination by collecting information from his/her social neighbours, which may either reinforce his/her own perceptions, or bring about conflicting perceptions.</p>
<p>In order to characterize individuals' belief value updates based on the obtained awareness and their subsequent vaccination decision-making in the presence of uncertainty, we develop a new <italic>belief–decision</italic>
model by extending the framework of the Dempster–Shafer theory (DST) [<xref ref-type="bibr" rid="RSIF20140013C45">45</xref>
]. DST, also known as theory of beliefs, was originally proposed as a generalization of the Bayesian theory of subjective probability to characterize how individuals update their beliefs by combining new pieces of evidence from multiple sources in the presence of uncertainty [<xref ref-type="bibr" rid="RSIF20140013C46">46</xref>
]. In our proposed DST-based belief–decision model, individuals can update their beliefs (i.e. with respect to perceived disease severity and vaccine safety) by combining the collected new evidence from social neighbours (i.e. the obtained awareness about disease and vaccine). Furthermore, we extend the conventional DST framework by incorporating the effect of awareness spreading, in which a reported event about disease or vaccine (i.e. a piece of new evidence) will ripple through individuals' social network. In this regard, individuals' vaccination decision-making is modelled as a process being affected by the spread of awareness about disease and vaccine as well as the subsequent updates of individuals' beliefs (i.e. the belief values of both yes and no).</p>
<p>We parametrize our proposed model with an influenza-like disease as well as a social network from a real-world online community. By carrying out a series of simulations on voluntary vaccination and infectious disease transmissions, we evaluate the impacts of the spread of awareness on individuals' vaccination decisions as well as its consequence on disease transmission dynamics with respect to the following two impact factors: (i) reporting rates of disease- and vaccine-related events, which denote the probabilities for an infected or vaccinated individual to be reported as a case of severe disease infections or vaccine-related AEFI and (ii) fading coefficient of awareness spread, which describes the effect of certainty decay when the awareness spreads from one person to another.</p>
</sec>
<sec id="s2"><label>2.</label>
<title>Models</title>
<p>We consider a voluntary vaccination programme for preventing the outbreak of an emerging infectious disease, e.g. 2009 H1N1 influenza, in which individuals can decide whether or not to take vaccine based on their awareness about disease severity and vaccine safety. It is assumed that individuals do not possess any prior knowledge about disease and vaccine, whereas they can receive information about disease- and vaccine-related events (i.e. the reported severe disease infections and vaccine-related AEFI). In this situation, the reported event about either disease or vaccine will trigger the spread of awareness among the host individuals, rippling through their interaction relationships, which will, in turn, affect their vaccination decisions.</p>
<p>For such a situation, we construct a new individual-based <italic>belief–decision</italic>
model to characterize vaccination decision-making. At the same time, we use an epidemic model to describe the dynamics of disease transmission as a result of individuals' voluntary vaccination. Based on our constructed model, we aim to investigate the impacts of the spread of awareness on the changes of individuals' vaccination decisions with respect to an emerging infectious disease. The parameters as used in the proposed model are summarized in <xref ref-type="table" rid="RSIF20140013TB1">table 1</xref>
.
<table-wrap id="RSIF20140013TB1" position="float"><label>Table 1.</label>
<caption><p>Parameters in the belief–decision model.</p>
</caption>
<table frame="hsides" rules="groups"><colgroup><col align="left"></col>
<col align="left"></col>
</colgroup>
<thead valign="top"><tr><th align="left">symbol</th>
<th align="left">description</th>
</tr>
</thead>
<tbody><tr><td><italic>m</italic>
(Yes)</td>
<td>belief value of vaccination</td>
</tr>
<tr><td><italic>m</italic>
(No)</td>
<td>belief value of non-vaccination</td>
</tr>
<tr><td><inline-formula><inline-graphic xlink:href="rsif20140013ileq34"></inline-graphic>
</inline-formula>
</td>
<td>belief value of no decision (uncertainty)</td>
</tr>
<tr><td><italic>m<sub>i</sub>
</italic>
</td>
<td>set of belief values</td>
</tr>
<tr><td><inline-formula><inline-graphic xlink:href="rsif20140013ileq35"></inline-graphic>
</inline-formula>
</td>
<td>obtained awareness about disease and vaccine</td>
</tr>
<tr><td><italic>ρ</italic>
</td>
<td>fading coefficient of awareness spread</td>
</tr>
<tr><td><inline-formula><inline-graphic xlink:href="rsif20140013ileq36"></inline-graphic>
</inline-formula>
</td>
<td>reporting rate of severe disease infections</td>
</tr>
<tr><td><italic>κ</italic>
</td>
<td>reporting rate of vaccine-related AEFI</td>
</tr>
<tr><td><italic>S</italic>
(<italic>t</italic>
)</td>
<td>number of susceptible individuals</td>
</tr>
<tr><td><italic>I</italic>
(<italic>t</italic>
)</td>
<td>number of infectious individuals</td>
</tr>
<tr><td><italic>R</italic>
(<italic>t</italic>
)</td>
<td>number of recovered/immunized individuals</td>
</tr>
<tr><td><italic>N</italic>
</td>
<td>total number of host individuals</td>
</tr>
<tr><td><italic>λ</italic>
(<italic>t</italic>
)</td>
<td>probability of disease infection</td>
</tr>
<tr><td><italic>β</italic>
</td>
<td>disease transmission rate</td>
</tr>
<tr><td><italic>γ</italic>
</td>
<td>infection recovery rate</td>
</tr>
<tr><td><italic>R</italic>
<sub>0</sub>
</td>
<td>basic reproduction number</td>
</tr>
</tbody>
</table>
</table-wrap>
</p>
<sec id="s2a"><label>2.1.</label>
<title>The belief–decision model</title>
<p>As described above, we have considered three possible vaccination-related decisions, i.e. to accept, to reject and yet to decide. For each individual, we first represent his/her willingness to accept or reject vaccine by using a set of belief variables. In order to characterize the state of ‘yet to decide’, we introduce the notion of decision-making with uncertainty based on the DST [<xref ref-type="bibr" rid="RSIF20140013C45">45</xref>
]. DST can be viewed as a generalization of the Bayesian theory of probability. Unlike the Bayesian theory, DST explicitly allows for an undecided state with respect to the presently available knowledge. We suppose that the problem of whether or not to take vaccine is a binary problem, which is represented as <inline-formula><inline-graphic xlink:href="rsif20140013ileq1"></inline-graphic>
</inline-formula>
called the frame of discernment for the vaccination decisions (i.e. a universal set). Individuals' possible vaccination decision responses can be modelled as the subsets of <italic>Θ</italic>
, i.e. belonging to a power set, <inline-formula><inline-graphic xlink:href="rsif20140013ileq2"></inline-graphic>
</inline-formula>
. Next, we use a function <italic>m</italic>
(·) to assign a belief mass (i.e. probability) to each element of the power set 2<sup><italic>Θ</italic>
</sup>
, which is called the basic probability assignment (BPA). The mass <italic>m</italic>
(A) <inline-formula><inline-graphic xlink:href="rsif20140013ileq3"></inline-graphic>
</inline-formula>
denotes the proportion of support for the particular subset <italic>A</italic>
based on the currently available evidence or knowledge. The BPA has the following two properties: (i) the mass of empty set <italic>ϕ</italic>
is zero, and (ii) the masses of the power set add up to one<disp-formula id="RSIF20140013M21"><label>2.1</label>
<graphic xlink:href="rsif20140013eq21"></graphic>
</disp-formula>
Accordingly, the belief functions for an individual's vaccination decision responses can be expressed as follows<disp-formula id="RSIF20140013M22"><label>2.2</label>
<graphic xlink:href="rsif20140013eq22"></graphic>
</disp-formula>
where <italic>m</italic>
(Yes) describes an individual's belief that he/she should get vaccinated for preventing disease infection, <italic>m</italic>
(No) represents the belief that he/she should reject vaccination having considered the potential risk of vaccine-related AEFI, and <italic>m</italic>
(<italic>Θ</italic>
) denotes the belief that he/she is yet to decide whether or not to get vaccinated (i.e. owing to the uncertainty about disease and vaccine). Based on the above formulation, an individual will decide to take vaccine with the probability of <italic>m</italic>
(Yes), reject vaccine with the probability of <italic>m</italic>
(No), and have no firm decision with the probability of <italic>m</italic>
(<italic>Θ</italic>
). In addition, we assume individuals with the decision of vaccination will get vaccinated directly and, therefore, will either be successfully immunized or suffer from vaccine-related AEFI. Those with no decision will revise their decisions in the next time step. We then use the DST to characterize individuals' decision-making with reference to information from multiple sources.</p>
</sec>
<sec id="s2b"><label>2.2.</label>
<title>The spread of awareness</title>
<p>During the spread of an emerging infectious disease and the implementation of a vaccination programme, individuals' perceptions about disease severity and vaccine safety will be affected by the obtained awareness from their socially connected neighbours. For instance, a case of severe disease infection can be naturally regarded as the evidence that an individual should get vaccinated. On the other hand, the events of vaccine-related AEFI can be viewed as the evidence that vaccination may be rejected. Here, we use a belief value <italic>m<sup>e</sup>
</italic>
to denote a piece of evidence that is triggered from a newly reported disease- or vaccine-related event. In order to characterize the spread of awareness in a socially connected host population, we consider two rules of information dynamics: (i) information transmission that awareness will spread between two connected individuals, and (ii) awareness fading that the certainty of a piece of new evidence will be gradually lost as of each transmission [<xref ref-type="bibr" rid="RSIF20140013C47">47</xref>
].</p>
<p>We suppose that a group of individuals are socially interconnected through their social network, denoted by <inline-formula><inline-graphic xlink:href="rsif20140013ileq4"></inline-graphic>
</inline-formula>
where <inline-formula><inline-graphic xlink:href="rsif20140013ileq5"></inline-graphic>
</inline-formula>
is the set of nodes (i.e. individuals), and <inline-formula><inline-graphic xlink:href="rsif20140013ileq6"></inline-graphic>
</inline-formula>
is the set of links (i.e. social interaction relationships). <italic>N</italic>
is the total number of individuals. During an epidemic, each reported event will be treated as a piece of triggering evidence with a belief value of <inline-formula><inline-graphic xlink:href="rsif20140013ileq7"></inline-graphic>
</inline-formula>
where <inline-formula><inline-graphic xlink:href="rsif20140013ileq8"></inline-graphic>
</inline-formula>
and <inline-formula><inline-graphic xlink:href="rsif20140013ileq9"></inline-graphic>
</inline-formula>
for a reported case of severe disease infection- and vaccine-related AEFI, respectively. Individuals can detect the emerged new evidence by interacting with their social neighbours, update their belief values accordingly, and can further talk about it to others through their social networks. Additionally, the certainty about a piece of evidence will decay as it is transmitted from person to person, which is referred to as awareness fading. Here, we introduce a fading coefficient, <italic>ρ</italic>
, to indicate how fast the decay will be when transmitting a piece of evidence between two individuals. A larger value of <italic>ρ</italic>
corresponds to a faster decay (i.e. faster certainty fading). Therefore, the evidence that is transmitted (i.e. the spread of awareness) from individual <italic>j</italic>
to his/her socially connected neighbour <italic>i</italic>
can be computed as follows<disp-formula id="RSIF20140013M23"><label>2.3</label>
<graphic xlink:href="rsif20140013eq23"></graphic>
</disp-formula>
</p>
<p>In the course of disease transmissions and vaccination implementation, newly reported events will constitute new sources of evidence at different time steps. The spread of awareness about these events will cause an individual to continuously update his/her perceptions about disease and vaccine, and thus make his/her vaccination decision. Based on the obtained awareness, the individual will update his/her belief values (i.e. denoted by <inline-formula><inline-graphic xlink:href="rsif20140013ileq10"></inline-graphic>
</inline-formula>
) by combining the present belief values (i.e. denoted by <italic>m<sub>i</sub>
</italic>
) with the newly received evidence <inline-formula><inline-graphic xlink:href="rsif20140013ileq11"></inline-graphic>
</inline-formula>
. This can be expressed in the following form (with <inline-formula><inline-graphic xlink:href="rsif20140013ileq12"></inline-graphic>
</inline-formula>
denoting the combination operation):<disp-formula id="RSIF20140013M24"><label>2.4</label>
<graphic xlink:href="rsif20140013eq24"></graphic>
</disp-formula>
</p>
<p>Specifically, based on the assumption that these multiple sources are independent, the belief value update with respect to the extended Dempster rule of combination [<xref ref-type="bibr" rid="RSIF20140013C48">48</xref>
] will be performed as follows<disp-formula id="RSIF20140013M25"><label>2.5</label>
<graphic xlink:href="rsif20140013eq25"></graphic>
</disp-formula>
where <inline-formula><inline-graphic xlink:href="rsif20140013ileq13"></inline-graphic>
</inline-formula>
represents the basic belief mass associated with the conflicts of present beliefs and the newly received evidence. In the Dempster combination rule, the denominator, <inline-formula><inline-graphic xlink:href="rsif20140013ileq14"></inline-graphic>
</inline-formula>
is a normalization factor, which attributes the conflict probability mass to the universal set <inline-formula><inline-graphic xlink:href="rsif20140013ileq15"></inline-graphic>
</inline-formula>
. In the extreme case, when an individual with the belief values of {1.0, 0} incorporates the evidence with the belief values of {0, 1.0}, his/her updated belief values will become {0,0}, which means the two conflicting opinions will lead the value of the individual's uncertainty <inline-formula><inline-graphic xlink:href="rsif20140013ileq16"></inline-graphic>
</inline-formula>
to unity.</p>
<p>By doing so, we have developed a belief–decision model for characterizing individuals' vaccination decisions in the presence of uncertainty by using the DST. Furthermore, we have extended the classical DST framework by incorporating the spread of awareness in a structured host population, in which the certainty about a piece of evidence will decay as it is transmitted from person to person. For the sake of illustration, <xref ref-type="fig" rid="RSIF20140013F2">figure 2</xref>
shows the results of individuals' belief value updates with respect to the spread of awareness about two independently reported events on a synthetic lattice network.
<fig id="RSIF20140013F2"><label>Figure 2.</label>
<caption><p>Illustrations of awareness spreading and fading on a synthetic lattice network (i.e. 30 × 30 nodes). Individuals can interact with their socially connected neighbours. The awareness about the two reported events will spread independently in such a structured host population, which will affect individuals' belief values. Here, we use the colour of a cell to denote an individual's belief value in terms of the probability mass (i.e. <italic>m<sub>i</sub>
∈</italic>
(0, 1)). As for the source of a reported event, the probability mass is set as <italic>m<sub>i</sub>
</italic>
= 1.0. We use the parameter <italic>ρ</italic>
to denote the fading coefficient of awareness spread. A larger value of <italic>ρ</italic>
indicates a faster decay of certainty as the awareness spreads from one person to another. We demonstrate the effects of the awareness fading in terms of individuals' belief values (i.e. the colour of each cell) with respect to two considered coefficients: (<italic>a</italic>
) <italic>ρ</italic>
= 0.1 and (<italic>b</italic>
) <italic>ρ</italic>
= 0.2.</p>
</caption>
<graphic xlink:href="rsif20140013f02"></graphic>
</fig>
</p>
</sec>
<sec id="s2c"><label>2.3.</label>
<title>The epidemic model</title>
<p>We construct an epidemic model to characterize the spread of an emerging infectious disease in a host population, in which the events (e.g. severe disease infections and vaccine-related AEFI) will be reported and, hence, the awareness will spread among them. In doing so, we adopt a standard compartmental model, i.e. susceptible–infectious–recovered (SIR) model, to describe the dynamics of disease infection, in which individuals are grouped into one of three infection-associated, homo-mixed compartments: susceptible (<italic>S</italic>
), infectious (<italic>I</italic>
) and recovered/immunized (<italic>R</italic>
). Therefore, the dynamics of disease transmission, as reflected in the dynamically changing compartments <italic>S</italic>
(<italic>t</italic>
), <italic>I</italic>
(<italic>t</italic>
) and <italic>R</italic>
(<italic>t</italic>
), can be modelled by using the following equations<disp-formula id="RSIF20140013M26"><label>2.6</label>
<graphic xlink:href="rsif20140013eq26"></graphic>
</disp-formula>
</p>
<p>Here, <italic>β</italic>
is the disease transmission rate between the susceptible and infectious populations. <italic>γ</italic>
represents the recovery rate that is the percentage of infectious individuals who will be recovered per time unit. Based on the definition of basic reproduction number <inline-formula><inline-graphic xlink:href="rsif20140013ileq17"></inline-graphic>
</inline-formula>
transmission rate can be calculated as <inline-formula><inline-graphic xlink:href="rsif20140013ileq18"></inline-graphic>
</inline-formula>
. Thus, the probability of being infected for a susceptible individual, denoted by <italic>λ</italic>
(<italic>t</italic>
), can be computed as follows<disp-formula id="RSIF20140013M27"><label>2.7</label>
<graphic xlink:href="rsif20140013eq27"></graphic>
</disp-formula>
where <italic>I<sub>j</sub>
</italic>
denotes individual <italic>j</italic>
is infected at time <italic>t</italic>
, and <italic>N</italic>
is the total number of host individuals. Therefore, <inline-formula><inline-graphic xlink:href="rsif20140013ileq19"></inline-graphic>
</inline-formula>
is the ratio of the infectious population. In this situation, we suppose that there is a probability <inline-formula><inline-graphic xlink:href="rsif20140013ileq20"></inline-graphic>
</inline-formula>
for each newly infected individual to be reported as a case of severe disease infection.</p>
<p>In addition, for the sake of example, we assume that vaccine is available and adequate at the same time as disease onsite (i.e. the first case of disease infection being reported). Only the susceptible individuals can decide whether and when to be vaccinated. Once an individual is vaccinated, it is assumed that he/she will be completely immunized and move from the susceptible compartment to the recovered/immunized compartment. As for a vaccinated individual, there is a probability <italic>κ</italic>
for being announced as a case of vaccine-related AEFI.</p>
</sec>
</sec>
<sec id="s3" sec-type="results"><label>3.</label>
<title>Results</title>
<sec id="s3a"><label>3.1.</label>
<title>Basic scenario</title>
<p>For our simulations, we calibrate the parameters in the proposed epidemic model with reference to the scenario of the 2009 Hong Kong H1N1 influenza epidemic, in which basic reproduction number <italic>R</italic>
<sub>0</sub>
in the epidemic stage was estimated as <italic>R</italic>
<sub>0</sub>
= 1.5 [<xref ref-type="bibr" rid="RSIF20140013C49">49</xref>
], and the infectious duration was around 3.75 days (i.e. recovery rate <italic>γ</italic>
≈ 0.267) [<xref ref-type="bibr" rid="RSIF20140013C50">50</xref>
]. During the outbreak of H1N1 influenza in Hong Kong, there were more than 36 000 laboratory confirmed cases (i.e. as of September 2010), among which about 290 were identified as severe cases (i.e. the reporting rate of severe disease infections <inline-formula><inline-graphic xlink:href="rsif20140013ileq21"></inline-graphic>
</inline-formula>
was estimated as 0.805%) [<xref ref-type="bibr" rid="RSIF20140013C51">51</xref>
]. In Hong Kong, the outbreak of H1N1 infection appeared in September 2009 and, in the second wave of infection, there were far fewer infection cases during the winter of 2009–2010. The human swine influenza (HSI) vaccination programme was launched on 1 December 2009. The numbers of vaccinated individuals ever since are shown in <xref ref-type="fig" rid="RSIF20140013F3">figure 3</xref>
. As of 13 March 2010, more than 180 000 doses of HSI vaccines were administered to persons of various groups [<xref ref-type="bibr" rid="RSIF20140013C52">52</xref>
]. In the whole HSI vaccination programme, a total of 34 cases of AEFI were reported. The rate of AEFI was evaluated as 17.8 per 100 000 vaccinated individuals (i.e. the reporting rate of vaccine-related AEFI <italic>κ</italic>
was estimated as 0.0178%) [<xref ref-type="bibr" rid="RSIF20140013C53">53</xref>
].
<fig id="RSIF20140013F3"><label>Figure 3.</label>
<caption><p>The number of daily-vaccinated individuals during the human swine influenza (HSI) vaccination programme against the 2009 Hong Kong H1N1 influenza epidemic. This programme was launched on 1 December 2009. As of 13 March 2010, more than 180 000 doses of HSI vaccines were administered to persons of various groups [<xref ref-type="bibr" rid="RSIF20140013C52">52</xref>
]. The rate of AEFI was evaluated as 17.8 per 100 000 vaccinated individuals and the reporting rate of vaccine-related AEFI <italic>κ</italic>
was estimated as 0.0178%.</p>
</caption>
<graphic xlink:href="rsif20140013f03"></graphic>
</fig>
</p>
<p>We further construct a social network to characterize individuals' interaction relationships based on the data of a Facebook-like online community [<xref ref-type="bibr" rid="RSIF20140013C54">54</xref>
], in which the registered users can communicate with each other online through personal blogs and forum postings. In this network, there are in total 1899 nodes and 13 838 undirected links among them. As shown in the partial network snapshot of <xref ref-type="fig" rid="RSIF20140013F4">figure 4</xref>
, the nodes denote the registered users, and the links among them represent their interaction relationships in terms of sending and receiving at least one message. Based on such a network structure, we carry out a series of Monte Carlo simulations and experimentally examine the above-mentioned belief-based characterization of individuals' vaccination decision-making. In our simulation, we assume that the spreads of awareness and disease are simultaneous. Moreover, individuals who have decided either for or against vaccination will no longer change their decisions. Meanwhile, those with the state of ‘yet to decide’ will revise their decision-making in the following time steps (i.e. days). We run the simulations of each considered scenario for 1000 times to remove the stochastic effects on individuals' vaccination decision-making.
<fig id="RSIF20140013F4"><label>Figure 4.</label>
<caption><p>A partial snapshot of individuals' social network. We use a network structure to represent individuals' interaction relationships, based on the data of a Facebook-like online community [<xref ref-type="bibr" rid="RSIF20140013C54">54</xref>
]. In such a network, the nodes denote individuals and the links represent their interactions in terms of sending and receiving messages.</p>
</caption>
<graphic xlink:href="rsif20140013f04"></graphic>
</fig>
</p>
<p>The simulation results in <xref ref-type="fig" rid="RSIF20140013F5">figure 5</xref>
show the dynamics of disease transmission and individuals' voluntary vaccination for the first 50 days. In this scenario, we examine the patterns of the vaccination programme in terms of daily-vaccinated individuals. We observe that the number of vaccinated individuals increases steadily in the earlier days of a vaccination programme as individuals' uncertainty about vaccination decreases. However, the reported cases of vaccine-related AEFI significantly increase individuals' belief about non-vaccination, which leads to a sharp decrease in the number of daily-vaccinated individuals after it has peaked on day 12.
<fig id="RSIF20140013F5"><label>Figure 5.</label>
<caption><p>Basic scenarios for the Monte Carlo simulations of disease transmission and voluntary vaccination. (<italic>a</italic>
) The dynamics of disease transmissions in terms of the sizes of susceptible, infectious, recovered and vaccinated populations. (<italic>b</italic>
) The average belief values about vaccination in a host population, i.e. <italic>m</italic>
(Yes), <italic>m</italic>
(No) and <inline-formula><inline-graphic xlink:href="rsif20140013ileq32"></inline-graphic>
</inline-formula>
. (<italic>c</italic>
) The dynamics of voluntary vaccination in terms of the number of daily-vaccinated individuals.</p>
</caption>
<graphic xlink:href="rsif20140013f05"></graphic>
</fig>
</p>
</sec>
<sec id="s3b"><label>3.2.</label>
<title>The interplay of two dynamics</title>
<p>We run the proposed model with the above-mentioned parametrizations under various scenarios to reveal the interplay between the dynamics of disease transmission and individuals' vaccination. In doing so, we investigate the impacts of the spread of awareness about disease severity and vaccine safety in a host population by means of investigating various settings of the reporting rates of disease- and vaccine-related events <inline-formula><inline-graphic xlink:href="rsif20140013ileq22"></inline-graphic>
</inline-formula>
and <italic>κ</italic>
, and the fading coefficient of awareness spread <italic>ρ</italic>
.</p>
<p>As shown in <xref ref-type="fig" rid="RSIF20140013F6">figure 6</xref>
, we first investigate the reporting rates for negative events on severe disease infections (i.e. <inline-formula><inline-graphic xlink:href="rsif20140013ileq23"></inline-graphic>
</inline-formula>
) and vaccine-related AEFI (i.e. <italic>κ</italic>
) with respect to two levels: 1% and 0.1%. Here, we set <inline-formula><inline-graphic xlink:href="rsif20140013ileq24"></inline-graphic>
</inline-formula>
and <italic>κ</italic>
= 0.001 for the situation of disease scare, and similarity, <inline-formula><inline-graphic xlink:href="rsif20140013ileq25"></inline-graphic>
</inline-formula>
and <italic>κ</italic>
= 0.01 for the scenario of vaccine scare. Generally speaking, a relative higher reporting rate of severe disease infections will prompt individuals' tendency for vaccination (i.e. as shown in <xref ref-type="fig" rid="RSIF20140013F6">figure 6</xref>
<italic>a</italic>
, dashed curve), which will in turn reduce disease transmissions (i.e. as shown in <xref ref-type="fig" rid="RSIF20140013F6">figure 6</xref>
<italic>b</italic>
, dashed curve). Moreover, vaccination in the early stage will be more effective than that in the later stages. We can observe that when <inline-formula><inline-graphic xlink:href="rsif20140013ileq26"></inline-graphic>
</inline-formula>
, the difference in the number of vaccinated individuals between the situations of <italic>κ</italic>
= 0.001 and <italic>κ</italic>
= 0.01 (i.e. as shown in <xref ref-type="fig" rid="RSIF20140013F6">figure 6</xref>
<italic>a</italic>
, dashed curve and solid curve, respectively) is relatively small for the early stage of disease transmissions (i.e. before day 10). After that, the vaccination dynamics when <inline-formula><inline-graphic xlink:href="rsif20140013ileq27"></inline-graphic>
</inline-formula>
and <italic>κ</italic>
= 0.001 will peak at the level of more than 4% of individuals who will vaccinate themselves on day 15, whereas that of <inline-formula><inline-graphic xlink:href="rsif20140013ileq28"></inline-graphic>
</inline-formula>
and <italic>κ</italic>
= 0.01 will peak at 2% on day 11. Accordingly, we can observe that the disease dynamics in the situations of <inline-formula><inline-graphic xlink:href="rsif20140013ileq29"></inline-graphic>
</inline-formula>
and <inline-formula><inline-graphic xlink:href="rsif20140013ileq30"></inline-graphic>
</inline-formula>
(i.e. as shown in <xref ref-type="fig" rid="RSIF20140013F6">figure 6</xref>
<italic>b</italic>
, dashed curve and solid curve, respectively) have relatively low incidence rates at the peaks of disease infection, whereas the lasting periods of disease transmissions are different.
<fig id="RSIF20140013F6"><label>Figure 6.</label>
<caption><p>The impacts of reporting rates of disease- and vaccine-related events (i.e. severe disease infections <inline-formula><inline-graphic xlink:href="rsif20140013ileq33"></inline-graphic>
</inline-formula>
and vaccine-related AEFI <italic>κ</italic>
). (<italic>a</italic>
) The dynamics of voluntary vaccination (i.e. the number of daily-vaccinated individuals). (<italic>b</italic>
) The dynamics of disease transmissions (i.e. the number of infectious individuals on each day).</p>
</caption>
<graphic xlink:href="rsif20140013f06"></graphic>
</fig>
</p>
<p>Besides, we have investigated the effect of awareness fading with respect to different fading coefficients <italic>ρ</italic>
, the results of which are shown in <xref ref-type="fig" rid="RSIF20140013F7">figure 7</xref>
. We note that awareness fading can affect the dynamics of individuals' vaccination in terms of the number of vaccinated individuals and the time of individuals' vaccine administration. In our simulation, when the fading coefficient <italic>ρ</italic>
= 0.1, the number of daily-vaccinated individuals will peak on day 10 with the rate around 4% (i.e. as shown in <xref ref-type="fig" rid="RSIF20140013F7">figure 7</xref>
<italic>a</italic>
, solid curve). The vaccination rates will be around 2% and peak on day 12 and day 19, if the fading coefficients are set as <italic>ρ</italic>
= 0.4 and <italic>ρ</italic>
= 0.7, respectively (i.e. as shown in <xref ref-type="fig" rid="RSIF20140013F7">figure 7</xref>
<italic>a</italic>
, dashed curve and point curve). In this situation, we can observe that the spread of awareness with a weak fading effect (i.e. a smaller fading coefficient) will prompt individuals' vaccination and thus prevent disease transmissions effectively (i.e. as shown in <xref ref-type="fig" rid="RSIF20140013F7">figure 7</xref>
<italic>b</italic>
, solid curve when <italic>ρ</italic>
= 0.1).
<fig id="RSIF20140013F7"><label>Figure 7.</label>
<caption><p>The impacts of fading coefficient (i.e. <italic>ρ</italic>
) during the spread of awareness in a host population. (<italic>a</italic>
) The dynamics of voluntary vaccination (i.e. the number of daily-vaccinated individuals). (<italic>b</italic>
) The dynamics of disease transmissions (i.e. the number of infectious individuals on each day).</p>
</caption>
<graphic xlink:href="rsif20140013f07"></graphic>
</fig>
</p>
</sec>
</sec>
<sec id="s4" sec-type="discussion"><label>4.</label>
<title>Discussion</title>
<p>It has long been observed that the spread of awareness will affect individuals' health-related behaviour. For instance, individuals who are aware of disease infections may take measures to reduce their susceptibility or distance their social contacts to prevent themselves from disease infections [<xref ref-type="bibr" rid="RSIF20140013C47">47</xref>
,<xref ref-type="bibr" rid="RSIF20140013C55">55</xref>
]. In the context of vaccination, the spread of awareness about severe disease infections and vaccine-related AEFI will affect individuals' perceptions about disease and vaccine and, hence, substantially change their vaccination behaviour.</p>
<p>In understanding the public acceptance of a vaccination programme, empirical studies have identified a series of determinants associated with individuals' vaccination decisions, such as the previous experience of vaccination [<xref ref-type="bibr" rid="RSIF20140013C56">56</xref>
], the perceived risk of disease infection as well as the safety and efficacy of vaccine [<xref ref-type="bibr" rid="RSIF20140013C10">10</xref>
,<xref ref-type="bibr" rid="RSIF20140013C57">57</xref>
–<xref ref-type="bibr" rid="RSIF20140013C59">59</xref>
], the social influence from professional instructions or friends' recommendations [<xref ref-type="bibr" rid="RSIF20140013C9">9</xref>
,<xref ref-type="bibr" rid="RSIF20140013C56">56</xref>
,<xref ref-type="bibr" rid="RSIF20140013C58">58</xref>
] and the socio-economic-related considerations [<xref ref-type="bibr" rid="RSIF20140013C9">9</xref>
,<xref ref-type="bibr" rid="RSIF20140013C15">15</xref>
]. Mathematical models have been developed to describe individuals' vaccination decision-making during the spread of an infectious disease [<xref ref-type="bibr" rid="RSIF20140013C16">16</xref>
,<xref ref-type="bibr" rid="RSIF20140013C60">60</xref>
]. As mentioned earlier, the pay-off-based approaches that use the game-theoretical analysis have characterized individuals' rational vaccination decisions by means of exploring the herd immunity effects (i.e. vaccinating a proportion of the host population would decrease the infection risk for the rest of unvaccinated individuals [<xref ref-type="bibr" rid="RSIF20140013C5">5</xref>
,<xref ref-type="bibr" rid="RSIF20140013C61">61</xref>
]). For instance, Bauch <italic>et al.</italic>
[<xref ref-type="bibr" rid="RSIF20140013C14">14</xref>
,<xref ref-type="bibr" rid="RSIF20140013C17">17</xref>
] characterized individuals' voluntary vaccination as a modified minority game in favour of optimizing personal pay-offs. Cojocaru [<xref ref-type="bibr" rid="RSIF20140013C62">62</xref>
] extended the game-theoretical analysis of vaccination to a heterogeneous host population group. Perisic <italic>et al.</italic>
[<xref ref-type="bibr" rid="RSIF20140013C18">18</xref>
,<xref ref-type="bibr" rid="RSIF20140013C63">63</xref>
] further examined individuals' vaccination game with respect to their contact network structures. Besides, social and psychological aspects of human behaviour have also been taken into consideration, such as a social learning process [<xref ref-type="bibr" rid="RSIF20140013C20">20</xref>
] and imitation behaviour [<xref ref-type="bibr" rid="RSIF20140013C21">21</xref>
–<xref ref-type="bibr" rid="RSIF20140013C24">24</xref>
].</p>
<p>As a further step from the above-mentioned studies, here we have considered a belief-based characterization of individuals' vaccination decisions. In our proposed model, we have correlated individuals' subjective assessment of disease severity and vaccine safety with the dynamics of disease transmission and voluntary vaccination by exploring the awareness about disease and vaccine. Different from the existing belief-based studies, e.g. that of Coelho <italic>et al.</italic>
[<xref ref-type="bibr" rid="RSIF20140013C25">25</xref>
], we have characterized individuals' belief value updates as a result of the spread of awareness in a structured host population (i.e. the social network from an online community). In this case, we can represent the situation that individuals collect health-related information from online social media, and make the vaccination decisions according to their obtained awareness from socially connected neighbours [<xref ref-type="bibr" rid="RSIF20140013C37">37</xref>
,<xref ref-type="bibr" rid="RSIF20140013C44">44</xref>
]. Additionally, instead of the polarized decisions, i.e. either vaccination or not, we have introduced the third decision response in terms of ‘yet to decide’ and associated it with the belief value of uncertainty. By doing so, we have used and extended the DST to characterize individuals’ belief value updates in the presence of uncertainty.</p>
<p>By parametrizing the proposed model with the epidemiological scenario of the 2009 Hong Kong H1N1 influenza epidemic, we have carried out a series of simulations on disease transmissions and voluntary vaccination. Based on that, we have investigated the impacts of the spread of awareness on individuals' vaccination decisions with respect to two considered impact factors. First, the reporting rates of severe disease infections <inline-formula><inline-graphic xlink:href="rsif20140013ileq31"></inline-graphic>
</inline-formula>
and vaccine-related AEFI <italic>κ</italic>
were used to represent the frequencies of respective topics that will draw public attention on social media. Our simulation results have shown that the reporting rates will determine the number of vaccinated individuals. Second, we considered the fading coefficient of awareness spread <italic>ρ</italic>
, a parameter used to quantify the effect of certainty decay during the information flows among individuals. We have observed that fading coefficient can affect the time at which individuals will make their decisions as to taking vaccine or not accordingly. Specifically, a higher fading coefficient (i.e. a faster certainty decay) will significantly delay individuals' vaccination decision-making, which will, in turn, influence the coverage of a vaccination programme. Salathe <italic>et al.</italic>
[<xref ref-type="bibr" rid="RSIF20140013C37">37</xref>
] have earlier studied vaccination sentiments with online social media (i.e. Twitter) and have found that individuals' behaviour of sharing the same sentiment was correlated with the frequency of information flows among them.</p>
<p>Our study on computationally characterizing the impacts of the spread of awareness has practical implications for public health authorities to predict the extent of public acceptance of a vaccination programme in advance by exploring the decision-making models. In recent years, a growing number of individuals use the Internet-based communication services to obtain and share the health-related information [<xref ref-type="bibr" rid="RSIF20140013C64">64</xref>
,<xref ref-type="bibr" rid="RSIF20140013C65">65</xref>
]. This represents the growing power of analysing individuals' online communication data to track the events in real time during an epidemic. Salathe <italic>et al.</italic>
[<xref ref-type="bibr" rid="RSIF20140013C37">37</xref>
] collected individuals' communication messages from Twitter and assessed the public sentiments towards a novel vaccine. Henrich <italic>et al.</italic>
[<xref ref-type="bibr" rid="RSIF20140013C44">44</xref>
] used online comments to capture public attitudes about the H1N1 vaccine. Online social media have become an effective means for sharing and creating public perceptions about disease and vaccine, upon which our proposed vaccination decision-making models can readily be used to estimate public acceptance of a vaccination programme [<xref ref-type="bibr" rid="RSIF20140013C42">42</xref>
,<xref ref-type="bibr" rid="RSIF20140013C66">66</xref>
]. Thereafter, public health authorities will be able to adjust their vaccination strategies drawing on the model-based decision-making analysis, so as to improve the effectiveness of adopted strategies.</p>
<p>So far, our study has provided a modelling framework that incorporates the spread of awareness with the belief-based characterization of decision-making. It should be pointed out that the obtained results may depend on the specific assumptions that were made in the design of our model. First, we have assumed no prior knowledge about disease and vaccine. However, individuals' historical experience, e.g. vaccination against seasonal influenza, may affect their vaccination decisions in the face of an emerging infectious disease, e.g. the 2009 H1N1 influenza. We have also assumed that vaccination dynamics and disease dynamics were instantaneously developed as well as individuals' vaccination decision-making was carried out simultaneously. Individuals' asynchronized decision-making and delayed vaccination could affect the simulation results. In our proposed model, the spread of awareness only accounts for individuals' localized interactions (i.e. a Facebook-like online community as used in our example), whereas the global effect of public media has not been taken into account in this study. It would be interesting to extend the current model by incorporating a globalized spread of awareness; that is to say, each individual will be aware of a reported event of disease and vaccine with a certain probability.</p>
<p>Besides the above-mentioned limitations in our decision-making modelling, we have adopted a simplified SIR-based epidemic and vaccination model, in which vaccine efficacy and the possible lag between the vaccine administration and the attainment of immunity were not taken into account. Furthermore, infected individuals in the latency state with no symptoms could make mistakes in their decisions. These issues are worth further investigations in our future work.</p>
</sec>
</body>
<back><ack><title>Acknowledgement</title>
<p>The authors would like to thank the handling editor and the anonymous reviewers for the constructive comments. They would also like to acknowledge support from Hong Kong Research Grants Council (HKBU211212).</p>
</ack>
<ref-list><title>References</title>
<ref id="RSIF20140013C1"><label>1</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Anderson</surname>
<given-names>RM</given-names>
</name>
<name><surname>May</surname>
<given-names>RM</given-names>
</name>
</person-group>
. <year>1982</year>
<article-title>Directly transmitted infections diseases: control by vaccination</article-title>
. <source>Science</source>
<volume>215</volume>
, <fpage>1053</fpage>
–<lpage>1060</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1126/science.7063839">doi:10.1126/science.7063839</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C2"><label>2</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Anderson</surname>
<given-names>RM</given-names>
</name>
<name><surname>May</surname>
<given-names>RM</given-names>
</name>
</person-group>
. <year>1985</year>
<article-title>Vaccination and herd immunity to infectious diseases</article-title>
. <source>Nature</source>
<volume>318</volume>
, <fpage>323</fpage>
–<lpage>329</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1038/318323a0">doi:10.1038/318323a0</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C3"><label>3</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Ferguson</surname>
<given-names>NM</given-names>
</name>
<name><surname>Cummings</surname>
<given-names>DAT</given-names>
</name>
<name><surname>Fraser</surname>
<given-names>C</given-names>
</name>
<name><surname>Cajka</surname>
<given-names>JC</given-names>
</name>
<name><surname>Cooley</surname>
<given-names>PC</given-names>
</name>
<name><surname>Burke</surname>
<given-names>DS</given-names>
</name>
</person-group>
. <year>2006</year>
<article-title>Strategies for mitigating an influenza pandemic</article-title>
. <source>Nature</source>
<volume>442</volume>
, <fpage>448</fpage>
–<lpage>452</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1038/nature04795">doi:10.1038/nature04795</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C4"><label>4</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Longini</surname>
<given-names>IM</given-names>
</name>
<etal></etal>
</person-group>
<year>2005</year>
<article-title>Containing pandemic influenza at the source</article-title>
. <source>Science</source>
<volume>309</volume>
, <fpage>1083</fpage>
–<lpage>1087</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1126/science.1115717">doi:10.1126/science.1115717</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C5"><label>5</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>John</surname>
<given-names>TJ</given-names>
</name>
<name><surname>Samuel</surname>
<given-names>R</given-names>
</name>
</person-group>
. <year>2000</year>
<article-title>Herd immunity and herd effect: new insights and definitions</article-title>
. <source>Eur. J. Epidemiol.</source>
<volume>16</volume>
, <fpage>601</fpage>
–<lpage>606</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1023/A:1007626510002">doi:10.1023/A:1007626510002</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C6"><label>6</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Fine</surname>
<given-names>P</given-names>
</name>
<name><surname>Eames</surname>
<given-names>K</given-names>
</name>
<name><surname>Heymann</surname>
<given-names>DL</given-names>
</name>
</person-group>
. <year>2011</year>
<article-title>‘Herd immunity’: a rough guide</article-title>
. <source>Clin. Infect. Dis.</source>
<volume>52</volume>
, <fpage>911</fpage>
–<lpage>916</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1093/cid/cir007">doi:10.1093/cid/cir007</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C7"><label>7</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Larson</surname>
<given-names>HJ</given-names>
</name>
<name><surname>Cooper</surname>
<given-names>LZ</given-names>
</name>
<name><surname>Eskola</surname>
<given-names>J</given-names>
</name>
<name><surname>Katz</surname>
<given-names>SL</given-names>
</name>
<name><surname>Ratzan</surname>
<given-names>S</given-names>
</name>
</person-group>
. <year>2011</year>
<article-title>Addressing the vaccine confidence gap</article-title>
. <source>Lancet</source>
<volume>378</volume>
, <fpage>526</fpage>
–<lpage>535</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1016/S0140-6736(11)60678-8">doi:10.1016/S0140-6736(11)60678-8</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C8"><label>8</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Black</surname>
<given-names>S</given-names>
</name>
<name><surname>Rappuoli</surname>
<given-names>R</given-names>
</name>
</person-group>
. <year>2010</year>
<article-title>A crisis of public confidence in vaccines</article-title>
. <source>Sci. Transl. Med.</source>
<volume>2</volume>
, <fpage>61mr1</fpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1126/scitranslmed.3001738">doi:10.1126/scitranslmed.3001738</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C9"><label>9</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Myers</surname>
<given-names>LB</given-names>
</name>
<name><surname>Goodwin</surname>
<given-names>R</given-names>
</name>
</person-group>
. <year>2011</year>
<article-title>Determinants of adults intention to vaccinate against pandemic swine flu</article-title>
. <source>BMC Public Health</source>
<volume>11</volume>
, <fpage>11C15</fpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1186/1471-2458-11-15">doi:10.1186/1471-2458-11-15</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C10"><label>10</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Eastwood</surname>
<given-names>K</given-names>
</name>
<name><surname>Durrheim</surname>
<given-names>DN</given-names>
</name>
<name><surname>Jones</surname>
<given-names>A</given-names>
</name>
<name><surname>Butler</surname>
<given-names>M</given-names>
</name>
</person-group>
. <year>2010</year>
<article-title>Acceptance of pandemic (H1N1) 2009 influenza vaccination by the Australian public</article-title>
. <source>Med. J. Aust.</source>
<volume>192</volume>
, <fpage>33</fpage>
–<lpage>36</lpage>
.</citation>
</ref>
<ref id="RSIF20140013C11"><label>11</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Liao</surname>
<given-names>Q</given-names>
</name>
<name><surname>Cowling</surname>
<given-names>BJ</given-names>
</name>
<name><surname>Lam</surname>
<given-names>WWT</given-names>
</name>
<name><surname>Fielding</surname>
<given-names>R</given-names>
</name>
</person-group>
. <year>2011</year>
<article-title>Factors affecting intention to receive and self-reported receipt of 2009 pandemic (H1N1) vaccine in Hong Kong: a longitudinal study</article-title>
. <source>PLoS ONE</source>
<volume>6</volume>
, <fpage>e17713</fpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1371/journal.pone.0017713">doi:10.1371/journal.pone.0017713</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C12"><label>12</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Streefland</surname>
<given-names>PH</given-names>
</name>
</person-group>
. <year>2001</year>
<article-title>Public doubts about vaccination safety and resistance against vaccination</article-title>
. <source>Health Policy</source>
<volume>55</volume>
, <fpage>159C172</fpage>
.</citation>
</ref>
<ref id="RSIF20140013C13"><label>13</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Francois</surname>
<given-names>G</given-names>
</name>
<etal></etal>
</person-group>
<year>2005</year>
<article-title>Vaccine safety controversies and the future of vaccination programs</article-title>
. <source>Pediatr. Infect. Dis. J.</source>
<volume>24</volume>
, <fpage>953</fpage>
–<lpage>961</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1097/01.inf.0000183853.16113.a6">doi:10.1097/01.inf.0000183853.16113.a6</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C14"><label>14</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Bauch</surname>
<given-names>CT</given-names>
</name>
<name><surname>Earn</surname>
<given-names>DJD</given-names>
</name>
</person-group>
. <year>2004</year>
<article-title>Vaccination and the theory of games</article-title>
. <source>Proc. Natl Acad. Sci. USA</source>
<volume>101</volume>
, <fpage>13 391</fpage>
–<lpage>13 394</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1073/pnas.0403823101">doi:10.1073/pnas.0403823101</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C15"><label>15</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Lau</surname>
<given-names>JTF</given-names>
</name>
<name><surname>Yeung</surname>
<given-names>NCY</given-names>
</name>
<name><surname>Choi</surname>
<given-names>KC</given-names>
</name>
<name><surname>Cheng</surname>
<given-names>MY</given-names>
</name>
<name><surname>Tsui</surname>
<given-names>HY</given-names>
</name>
<name><surname>Griffiths</surname>
<given-names>S</given-names>
</name>
</person-group>
. <year>2009</year>
<article-title>Acceptability of A/H1N1 vaccination during pandemic phase of influenza A/H1N1 in Hong Kong: population based cross sectional survey</article-title>
. <source>BMJ</source>
<volume>339</volume>
, <fpage>b4164</fpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1136/bmj.b4164">doi:10.1136/bmj.b4164</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C16"><label>16</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Funk</surname>
<given-names>S</given-names>
</name>
<name><surname>Salathé</surname>
<given-names>M</given-names>
</name>
<name><surname>Jansen</surname>
<given-names>VAA</given-names>
</name>
</person-group>
. <year>2010</year>
<article-title>Modelling the influence of human behaviour on the spread of infectious diseases: a review</article-title>
. <source>J. R. Soc. Interface</source>
<volume>7</volume>
, <fpage>1247</fpage>
–<lpage>1256</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1098/rsif.2010.0142">doi:10.1098/rsif.2010.0142</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C17"><label>17</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Bauch</surname>
<given-names>CT</given-names>
</name>
<name><surname>Galvani</surname>
<given-names>AP</given-names>
</name>
<name><surname>Earn</surname>
<given-names>DJD</given-names>
</name>
</person-group>
. <year>2003</year>
<article-title>Group interest versus self-interest in smallpox vaccination policy</article-title>
. <source>Proc. Natl Acad. Sci. USA</source>
<volume>100</volume>
, <fpage>10 564</fpage>
–<lpage>10 567</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1073/pnas.1731324100">doi:10.1073/pnas.1731324100</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C18"><label>18</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Perisic</surname>
<given-names>A</given-names>
</name>
<name><surname>Bauch</surname>
<given-names>CT</given-names>
</name>
</person-group>
. <year>2009</year>
<article-title>Social contact networks and disease eradicability under voluntary vaccination</article-title>
. <source>PLoS Comput. Biol.</source>
<volume>5</volume>
, <fpage>e1000280</fpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1371/journal.pcbi.1000280">doi:10.1371/journal.pcbi.1000280</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C19"><label>19</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Reluga</surname>
<given-names>TC</given-names>
</name>
<name><surname>Galvani</surname>
<given-names>AP</given-names>
</name>
</person-group>
. <year>2011</year>
<article-title>A general approach for population games with application to vaccination</article-title>
. <source>Math. Biosci.</source>
<volume>230</volume>
, <fpage>67</fpage>
–<lpage>78</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1016/j.mbs.2011.01.003">doi:10.1016/j.mbs.2011.01.003</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C20"><label>20</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Bauch</surname>
<given-names>CT</given-names>
</name>
<name><surname>Bhattacharyya</surname>
<given-names>S</given-names>
</name>
</person-group>
. <year>2012</year>
<article-title>Evolutionary game theory and social learning can determine how vaccine scares unfold</article-title>
. <source>PLoS Comput. Biol.</source>
<volume>8</volume>
, <fpage>e1002452</fpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1371/journal.pcbi.1002452">doi:10.1371/journal.pcbi.1002452</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C21"><label>21</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Bauch</surname>
<given-names>CT</given-names>
</name>
</person-group>
. <year>2005</year>
<article-title>Imitation dynamics predict vaccinating behaviour</article-title>
. <source>Proc. R Soc. B</source>
<volume>272</volume>
, <fpage>1669</fpage>
–<lpage>1675</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1098/rspb.2005.3153">doi:10.1098/rspb.2005.3153</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C22"><label>22</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Fu</surname>
<given-names>F</given-names>
</name>
<name><surname>Rosenbloom</surname>
<given-names>DI</given-names>
</name>
<name><surname>Wang</surname>
<given-names>L</given-names>
</name>
<name><surname>Nowak</surname>
<given-names>MA</given-names>
</name>
</person-group>
. <year>2011</year>
<article-title>Imitation dynamics of vaccination behaviour on social networks</article-title>
. <source>Proc. R. Soc. B</source>
<volume>278</volume>
, <fpage>42</fpage>
–<lpage>49</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1098/rspb.2010.1107">doi:10.1098/rspb.2010.1107</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C23"><label>23</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>d'Onofrio</surname>
<given-names>A</given-names>
</name>
<name><surname>Manfredi</surname>
<given-names>P</given-names>
</name>
<name><surname>Poletti</surname>
<given-names>P</given-names>
</name>
</person-group>
. <year>2011</year>
<article-title>The impact of vaccine side effects on the natural history of immunization programmes: an imitation-game approach</article-title>
. <source>J. Theor. Biol.</source>
<volume>273</volume>
, <fpage>63</fpage>
–<lpage>71</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1016/j.jtbi.2010.12.029">doi:10.1016/j.jtbi.2010.12.029</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C24"><label>24</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Mbah</surname>
<given-names>MLN</given-names>
</name>
<name><surname>Liu</surname>
<given-names>J</given-names>
</name>
<name><surname>Bauch</surname>
<given-names>CT</given-names>
</name>
<name><surname>Tekel</surname>
<given-names>YI</given-names>
</name>
<name><surname>Medlock</surname>
<given-names>J</given-names>
</name>
<name><surname>Meyers</surname>
<given-names>LA</given-names>
</name>
<name><surname>Galvani</surname>
<given-names>AP</given-names>
</name>
</person-group>
. <year>2012</year>
<article-title>The impact of imitation on vaccination behavior in social contact networks</article-title>
. <source>PLoS Comput. Biol.</source>
<volume>8</volume>
, <fpage>e1002469</fpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1371/journal.pcbi.1002469">doi:10.1371/journal.pcbi.1002469</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C25"><label>25</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Coelho</surname>
<given-names>FC</given-names>
</name>
<name><surname>Codeco</surname>
<given-names>CT</given-names>
</name>
</person-group>
. <year>2009</year>
<article-title>Dynamic modeling of vaccinating behavior as a function of individual beliefs</article-title>
. <source>PLoS Comput. Biol.</source>
<volume>5</volume>
, <fpage>e1000425</fpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1371/journal.pcbi.1000425">doi:10.1371/journal.pcbi.1000425</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C26"><label>26</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Zhang</surname>
<given-names>H</given-names>
</name>
<name><surname>Zhang</surname>
<given-names>J</given-names>
</name>
<name><surname>Li</surname>
<given-names>P</given-names>
</name>
<name><surname>Small</surname>
<given-names>M</given-names>
</name>
<name><surname>Wang</surname>
<given-names>B</given-names>
</name>
</person-group>
. <year>2011</year>
<article-title>Risk estimation of infectious diseases determines the effectiveness of the control strategy</article-title>
. <source>Physica D</source>
<volume>240</volume>
, <fpage>943</fpage>
–<lpage>948</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1016/j.physd.2011.02.001">doi:10.1016/j.physd.2011.02.001</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C27"><label>27</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>d'Onofrio</surname>
<given-names>A</given-names>
</name>
<name><surname>Manfredi</surname>
<given-names>P</given-names>
</name>
<name><surname>Salinelli</surname>
<given-names>E</given-names>
</name>
</person-group>
. <year>2007</year>
<article-title>Vaccinating behaviour, information, and the dynamics of SIR vaccine preventable diseases</article-title>
. <source>Theor. Popul. Biol.</source>
<volume>71</volume>
, <fpage>301</fpage>
–<lpage>317</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1016/j.tpb.2007.01.001">doi:10.1016/j.tpb.2007.01.001</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C28"><label>28</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Breban</surname>
<given-names>R</given-names>
</name>
<name><surname>Vardavas</surname>
<given-names>R</given-names>
</name>
<name><surname>Blower</surname>
<given-names>S</given-names>
</name>
</person-group>
. <year>2007</year>
<article-title>Mean-field analysis of an inductive reasoning game: application to influenza vaccination</article-title>
. <source>Phys. Rev. E</source>
<volume>76</volume>
, <fpage>031127</fpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1103/PhysRevE.76.031127">doi:10.1103/PhysRevE.76.031127</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C29"><label>29</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>d'Onofrio</surname>
<given-names>A</given-names>
</name>
<name><surname>Manfredi</surname>
<given-names>P</given-names>
</name>
</person-group>
. <year>2010</year>
<article-title>Vaccine demand driven by vaccine side effects: dynamic implications for SIR diseases</article-title>
. <source>J. Theor. Biol.</source>
<volume>264</volume>
, <fpage>237</fpage>
–<lpage>252</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1016/j.jtbi.2010.02.007">doi:10.1016/j.jtbi.2010.02.007</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C30"><label>30</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Grassly</surname>
<given-names>NC</given-names>
</name>
<name><surname>Fraser</surname>
<given-names>C</given-names>
</name>
</person-group>
. <year>2006</year>
<article-title>Seasonal infectious disease epidemiology</article-title>
. <source>Proc. R. Soc. B</source>
<volume>273</volume>
, <fpage>2541</fpage>
–<lpage>2550</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1098/rspb.2006.3604">doi:10.1098/rspb.2006.3604</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C31"><label>31</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>SteelFisher</surname>
<given-names>GK</given-names>
</name>
<name><surname>Blendon</surname>
<given-names>RJ</given-names>
</name>
<name><surname>Bekheit</surname>
<given-names>MM</given-names>
</name>
<name><surname>Lubell</surname>
<given-names>K</given-names>
</name>
</person-group>
. <year>2010</year>
<article-title>The public's response to the 2009 H1N1 influenza pandemic</article-title>
. <source>N. Engl. J. Med.</source>
<volume>362</volume>
, <fpage>e65</fpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1056/NEJMp1005102">doi:10.1056/NEJMp1005102</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C32"><label>32</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Bish</surname>
<given-names>A</given-names>
</name>
<name><surname>Yardley</surname>
<given-names>L</given-names>
</name>
<name><surname>Nicoll</surname>
<given-names>A</given-names>
</name>
<name><surname>Michie</surname>
<given-names>S</given-names>
</name>
</person-group>
. <year>2011</year>
<article-title>Factors associated with uptake of vaccination against pandemic influenza: a systematic review</article-title>
. <source>Vaccine</source>
<volume>29</volume>
, <fpage>6472</fpage>
–<lpage>6484</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1016/j.vaccine.2011.06.107">doi:10.1016/j.vaccine.2011.06.107</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C33"><label>33</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Goldstein</surname>
<given-names>KP</given-names>
</name>
<name><surname>Philipson</surname>
<given-names>TJ</given-names>
</name>
<name><surname>Joo</surname>
<given-names>H</given-names>
</name>
<name><surname>Daum</surname>
<given-names>RS</given-names>
</name>
</person-group>
. <year>1996</year>
<article-title>The effect of epidemic measles on immunization rates</article-title>
. <source>JAMA</source>
<volume>276</volume>
, <fpage>56</fpage>
–<lpage>58</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1001/jama.1996.03540010058031">doi:10.1001/jama.1996.03540010058031</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C34"><label>34</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Roberts</surname>
<given-names>RJ</given-names>
</name>
<name><surname>Sandifer</surname>
<given-names>QD</given-names>
</name>
<name><surname>Evans</surname>
<given-names>MR</given-names>
</name>
<name><surname>Nolan-Farrell</surname>
<given-names>MZ</given-names>
</name>
<name><surname>Davis</surname>
<given-names>PM</given-names>
</name>
</person-group>
. <year>1995</year>
<article-title>Reasons for non-uptake of measles, mumps, and rubella catch up immunisation in a measles epidemic and side effects of the vaccine</article-title>
. <source>BMJ</source>
<volume>310</volume>
, <fpage>1629</fpage>
–<lpage>1632</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1136/bmj.310.6995.1629">doi:10.1136/bmj.310.6995.1629</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C35"><label>35</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Anderson</surname>
<given-names>P</given-names>
</name>
</person-group>
. <year>1999</year>
<article-title>Another media scare about MMR vaccine hits Britain</article-title>
. <source>BMJ</source>
<volume>318</volume>
, <fpage>1578</fpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1136/bmj.318.7198.1578">doi:10.1136/bmj.318.7198.1578</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C36"><label>36</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Young</surname>
<given-names>ME</given-names>
</name>
<name><surname>Norman</surname>
<given-names>GR</given-names>
</name>
<name><surname>Humphreys</surname>
<given-names>KR</given-names>
</name>
</person-group>
. <year>2008</year>
<article-title>Medicine in the popular press: the influence of the media on perceptions of disease</article-title>
. <source>PLoS ONE</source>
<volume>3</volume>
, <fpage>e3552</fpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1371/journal.pone.0003552">doi:10.1371/journal.pone.0003552</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C37"><label>37</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Salathe</surname>
<given-names>M</given-names>
</name>
<name><surname>Khandelwal</surname>
<given-names>S</given-names>
</name>
</person-group>
. <year>2011</year>
<article-title>Assessing vaccination sentiments with online social media: implications for infectious disease dynamics and control</article-title>
. <source>PLoS Comput. Biol.</source>
<volume>7</volume>
, <fpage>e1002199</fpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1371/journal.pcbi.1002199">doi:10.1371/journal.pcbi.1002199</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C38"><label>38</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Vance</surname>
<given-names>K</given-names>
</name>
<name><surname>Howe</surname>
<given-names>W</given-names>
</name>
<name><surname>Dellavalle</surname>
<given-names>RP</given-names>
</name>
</person-group>
. <year>2009</year>
<article-title>Social internet sites as a source of public health information</article-title>
. <source>Dermatol. Clin.</source>
<volume>27</volume>
, <fpage>133</fpage>
–<lpage>136</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1016/j.det.2008.11.010">doi:10.1016/j.det.2008.11.010</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C39"><label>39</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Signorini</surname>
<given-names>A</given-names>
</name>
<name><surname>Segre</surname>
<given-names>AM</given-names>
</name>
<name><surname>Polgreen</surname>
<given-names>PM</given-names>
</name>
</person-group>
. <year>2011</year>
<article-title>The use of Twitter to track levels of disease activity and public concern in the US during the influenza A H1N1 pandemic</article-title>
. <source>PLoS ONE</source>
<volume>6</volume>
, <fpage>e19467</fpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1371/journal.pone.0019467">doi:10.1371/journal.pone.0019467</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C40"><label>40</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Keelan</surname>
<given-names>J</given-names>
</name>
<name><surname>Pavri-Garcia</surname>
<given-names>V</given-names>
</name>
<name><surname>Tomlinson</surname>
<given-names>G</given-names>
</name>
<name><surname>Wilson</surname>
<given-names>K</given-names>
</name>
</person-group>
. <year>2007</year>
<article-title>YouTube as a source of information on immunization: a content analysis</article-title>
. <source>JAMA</source>
<volume>298</volume>
, <fpage>2482</fpage>
–<lpage>2484</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1001/jama.298.21.2482">doi:10.1001/jama.298.21.2482</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C41"><label>41</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Pandey</surname>
<given-names>A</given-names>
</name>
<name><surname>Patni</surname>
<given-names>N</given-names>
</name>
<name><surname>Singh</surname>
<given-names>M</given-names>
</name>
<name><surname>Sood</surname>
<given-names>A</given-names>
</name>
<name><surname>Singh</surname>
<given-names>G</given-names>
</name>
</person-group>
. <year>2010</year>
<article-title>YouTube as a source of information on the H1N1 influenza pandemic</article-title>
. <source>Am. J. Prev. Med.</source>
<volume>38</volume>
, <fpage>e1</fpage>
–<lpage>e3</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1016/j.amepre.2009.11.007">doi:10.1016/j.amepre.2009.11.007</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C42"><label>42</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Witteman</surname>
<given-names>HO</given-names>
</name>
<name><surname>Zikmund-Fisher</surname>
<given-names>BJ</given-names>
</name>
</person-group>
. <year>2011</year>
<article-title>The defining characteristics of Web 2.0 and their potential influence in the online vaccination debate</article-title>
. <source>Vaccine</source>
<volume>30</volume>
, <fpage>3734</fpage>
–<lpage>3740</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1016/j.vaccine.2011.12.039">doi:10.1016/j.vaccine.2011.12.039</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C43"><label>43</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Keelan</surname>
<given-names>J</given-names>
</name>
<name><surname>Pavri</surname>
<given-names>V</given-names>
</name>
<name><surname>Balakrishnan</surname>
<given-names>R</given-names>
</name>
<name><surname>Wilson</surname>
<given-names>K</given-names>
</name>
</person-group>
. <year>2010</year>
<article-title>An analysis of the human papilloma virus vaccine debate on MySpace blogs</article-title>
. <source>Vaccine</source>
<volume>28</volume>
, <fpage>1535</fpage>
–<lpage>1540</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1016/j.vaccine.2009.11.060">doi:10.1016/j.vaccine.2009.11.060</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C44"><label>44</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Henrich</surname>
<given-names>N</given-names>
</name>
<name><surname>Holmes</surname>
<given-names>B</given-names>
</name>
</person-group>
. <year>2011</year>
<article-title>What the public was saying about the H1N1 vaccine: perceptions and issues discussed in on-line comments during the 2009 H1N1 pandemic</article-title>
. <source>PLoS ONE</source>
<volume>6</volume>
, <fpage>e18479</fpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1371/journal.pone.0018479">doi:10.1371/journal.pone.0018479</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C45"><label>45</label>
<citation citation-type="book"><person-group person-group-type="author"><name><surname>Shafer</surname>
<given-names>G</given-names>
</name>
</person-group>
. <year>1976</year>
<source>A mathematical theory of evidence</source>
, <volume>vol. 76</volume>
. <publisher-loc>Princeton, NJ</publisher-loc>
: <publisher-name>Princeton University Press</publisher-name>
.</citation>
</ref>
<ref id="RSIF20140013C46"><label>46</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Dempster</surname>
<given-names>AP</given-names>
</name>
</person-group>
. <year>1967</year>
<article-title>Upper and lower probabilities induced by a multivalued mapping</article-title>
. <source>Ann. Math. Stat.</source>
<volume>38</volume>
, <fpage>325</fpage>
–<lpage>339</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1214/aoms/1177698950">doi:10.1214/aoms/1177698950</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C47"><label>47</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Funk</surname>
<given-names>S</given-names>
</name>
<name><surname>Gilada</surname>
<given-names>E</given-names>
</name>
<name><surname>Watkinsb</surname>
<given-names>C</given-names>
</name>
<name><surname>Jansena</surname>
<given-names>VAA</given-names>
</name>
</person-group>
. <year>2009</year>
<article-title>The spread of awareness and its impact on epidemic outbreaks</article-title>
. <source>Proc. Natl Acad. Sci. USA</source>
<volume>106</volume>
, <fpage>6872</fpage>
–<lpage>6877</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1073/pnas.0810762106">doi:10.1073/pnas.0810762106</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C48"><label>48</label>
<citation citation-type="book"><person-group person-group-type="author"><name><surname>Sentz</surname>
<given-names>K</given-names>
</name>
<name><surname>Ferson</surname>
<given-names>S</given-names>
</name>
</person-group>
. <year>2002</year>
<source>Combination of evidence in Dempster–Shafer theory</source>
. <publisher-loc>Albuquerque, NM</publisher-loc>
: <publisher-name>Sandia National Laboratories</publisher-name>
.</citation>
</ref>
<ref id="RSIF20140013C49"><label>49</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Cowling</surname>
<given-names>BJ</given-names>
</name>
<name><surname>Lau</surname>
<given-names>MSY</given-names>
</name>
<name><surname>Ho</surname>
<given-names>LM</given-names>
</name>
<name><surname>Chuang</surname>
<given-names>SK</given-names>
</name>
<name><surname>Tsang</surname>
<given-names>T</given-names>
</name>
<name><surname>Liu</surname>
<given-names>SH</given-names>
</name>
<name><surname>Leung</surname>
<given-names>P-Y</given-names>
</name>
<name><surname>Lo</surname>
<given-names>S-V</given-names>
</name>
<name><surname>Lau</surname>
<given-names>EHY</given-names>
</name>
</person-group>
. <year>2010</year>
<article-title>The effective reproduction number of pandemic influenza: prospective estimation</article-title>
. <source>Epidemiology</source>
<volume>21</volume>
, <fpage>842</fpage>
–<lpage>846</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1097/EDE.0b013e3181f20977">doi:10.1097/EDE.0b013e3181f20977</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C50"><label>50</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Wu</surname>
<given-names>JT</given-names>
</name>
<etal></etal>
</person-group>
<year>2011</year>
<article-title>Estimating infection attack rates and severity in real time during an influenza pandemic: analysis of serial cross-sectional serologic surveillance data</article-title>
. <source>PLoS Med.</source>
<volume>8</volume>
, <fpage>e1001103</fpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1371/journal.pmed.1001103">doi:10.1371/journal.pmed.1001103</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C51"><label>51</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Wu</surname>
<given-names>JT</given-names>
</name>
<etal></etal>
</person-group>
<year>2010</year>
<article-title>The infection attack rate and severity of 2009 pandemic H1N1 influenza in Hong Kong</article-title>
. <source>Clin. Infect. Dis.</source>
<volume>51</volume>
, <fpage>1184</fpage>
–<lpage>1191</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1086/656740">doi:10.1086/656740</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C52"><label>52</label>
<citation citation-type="other"><collab>Information Services Department of the Hong Kong Government</collab>
. <comment>Human swine influenza vaccination programme</comment>
. See <comment><uri xlink:href="http://www.info.gov.hk/gia/general/201003/17/P201003170168.htm">http://www.info.gov.hk/gia/general/201003/17/P201003170168.htm</uri>
.</comment>
</citation>
</ref>
<ref id="RSIF20140013C53"><label>53</label>
<citation citation-type="other"><collab>Centre for Health Protection</collab>
. <comment>Summary report on the surveillance of adverse events following HSI immunisation and expert groups comment on the safety of HSI vaccine in Hong Kong;. September 2010. Online</comment>
. See <comment><uri xlink:href="http://www.chp.gov.hk/files/pdf/hsi_vaccine_aefi_report_en.pdf">http://www.chp.gov.hk/files/pdf/hsi_vaccine_aefi_report_en.pdf</uri>
.</comment>
</citation>
</ref>
<ref id="RSIF20140013C54"><label>54</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Panzarasa</surname>
<given-names>P</given-names>
</name>
<name><surname>Opsahl</surname>
<given-names>T</given-names>
</name>
<name><surname>Carley</surname>
<given-names>KM</given-names>
</name>
</person-group>
. <year>2009</year>
<article-title>Patterns and dynamics of users’ behavior and interaction: network analysis of an online community</article-title>
. <source>J. Am. Soc. Inf. Sci. Technol.</source>
<volume>60</volume>
, <fpage>911</fpage>
–<lpage>932</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1002/asi.v60:5">doi:10.1002/asi.v60:5</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C55"><label>55</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Fenichel</surname>
<given-names>EP</given-names>
</name>
<etal></etal>
</person-group>
<year>2011</year>
<article-title>Adaptive human behavior in epidemiological models</article-title>
. <source>Proc. Natl Acad. Sci. USA</source>
<volume>108</volume>
, <fpage>6306</fpage>
–<lpage>6311</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1073/pnas.1011250108">doi:10.1073/pnas.1011250108</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C56"><label>56</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Lau</surname>
<given-names>JTF</given-names>
</name>
<name><surname>Yeung</surname>
<given-names>NCY</given-names>
</name>
<name><surname>Choi</surname>
<given-names>MYMC</given-names>
</name>
<name><surname>Tsui</surname>
<given-names>HY</given-names>
</name>
<name><surname>Griffiths</surname>
<given-names>S</given-names>
</name>
</person-group>
. <year>2010</year>
<article-title>Factors in association with acceptability of A/H1N1 vaccination during the influenza A/H1N1 pandemic phase in the Hong Kong general population</article-title>
. <source>Vaccine</source>
<volume>28</volume>
, <fpage>4632C4637</fpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1016/j.vaccine.2010.04.076">doi:10.1016/j.vaccine.2010.04.076</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C57"><label>57</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Seale</surname>
<given-names>H</given-names>
</name>
<name><surname>Heywood</surname>
<given-names>AE</given-names>
</name>
<name><surname>McLaws</surname>
<given-names>ML</given-names>
</name>
<name><surname>Ward</surname>
<given-names>KF</given-names>
</name>
<name><surname>Lowbridge</surname>
<given-names>CP</given-names>
</name>
<name><surname>Van</surname>
<given-names>D</given-names>
</name>
<name><surname>MacIntyre</surname>
<given-names>CR</given-names>
</name>
</person-group>
. <year>2010</year>
<article-title>Why do I need it? I am not at risk! Public perceptions towards the pandemic (H1N1) 2009 vaccine</article-title>
. <source>BMC Infect. Dis.</source>
<volume>10</volume>
, <fpage>99</fpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1186/1471-2334-10-99">doi:10.1186/1471-2334-10-99</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C58"><label>58</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Zijtregtop</surname>
<given-names>EAM</given-names>
</name>
<etal></etal>
</person-group>
<year>2009</year>
<article-title>Which factors are important in adults uptake of a (pre)pandemic influenza vaccine?</article-title>
<source>Vaccine</source>
<volume>28</volume>
, <fpage>207</fpage>
–<lpage>227</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1016/j.vaccine.2009.09.099">doi:10.1016/j.vaccine.2009.09.099</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C59"><label>59</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Wong</surname>
<given-names>LP</given-names>
</name>
<name><surname>Samb</surname>
<given-names>IC</given-names>
</name>
</person-group>
. <year>2010</year>
<article-title>Factors influencing the uptake of 2009 H1N1 influenza vaccine in a multiethnic Asian population</article-title>
. <source>Vaccine</source>
<volume>28</volume>
, <fpage>4499</fpage>
–<lpage>4505</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1016/j.vaccine.2010.04.043">doi:10.1016/j.vaccine.2010.04.043</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C60"><label>60</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Perra</surname>
<given-names>N</given-names>
</name>
<name><surname>Balcan</surname>
<given-names>D</given-names>
</name>
<name><surname>Goncalves</surname>
<given-names>B</given-names>
</name>
<name><surname>Vespignani</surname>
<given-names>A</given-names>
</name>
</person-group>
. <year>2011</year>
<article-title>Towards a characterization of behavior-disease models</article-title>
. <source>PLoS ONE</source>
<volume>6</volume>
, <fpage>e23084</fpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1371/journal.pone.0023084">doi:10.1371/journal.pone.0023084</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C61"><label>61</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Stephens</surname>
<given-names>DS</given-names>
</name>
</person-group>
. <year>2008</year>
<article-title>Vaccines for the unvaccinated: protecting the herd</article-title>
. <source>J Infect. Dis.</source>
<volume>197</volume>
, <fpage>643</fpage>
–<lpage>645</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1086/527402">doi:10.1086/527402</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C62"><label>62</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Cojocaru</surname>
<given-names>MG</given-names>
</name>
</person-group>
. <year>2008</year>
<article-title>Dynamic equilibria of group vaccination strategies in a heterogeneous population</article-title>
. <source>J. Glob. Optim.</source>
<volume>40</volume>
, <fpage>51C63</fpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1007/s10898-007-9204-7">doi:10.1007/s10898-007-9204-7</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C63"><label>63</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Perisic</surname>
<given-names>A</given-names>
</name>
<name><surname>Bauch</surname>
<given-names>CT</given-names>
</name>
</person-group>
. <year>2009</year>
<article-title>A simulation analysis to characterize the dynamics of vaccinating behaviour on contact networks</article-title>
. <source>BMC Infect. Dis.</source>
<volume>9</volume>
, <fpage>77</fpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1186/1471-2334-9-77">doi:10.1186/1471-2334-9-77</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C64"><label>64</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Kaplan</surname>
<given-names>AM</given-names>
</name>
<name><surname>Haenlein</surname>
<given-names>M</given-names>
</name>
</person-group>
. <year>2010</year>
<article-title>Users of the world, unite! The challenges and opportunities of social media</article-title>
. <source>Bus. Horiz.</source>
<volume>53</volume>
, <fpage>59</fpage>
–<lpage>68</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1016/j.bushor.2009.09.003">doi:10.1016/j.bushor.2009.09.003</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C65"><label>65</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Ginsberg</surname>
<given-names>J</given-names>
</name>
<name><surname>Mohebbi</surname>
<given-names>MH</given-names>
</name>
<name><surname>Patel</surname>
<given-names>RS</given-names>
</name>
<name><surname>Brammer</surname>
<given-names>L</given-names>
</name>
<name><surname>Smolinski</surname>
<given-names>MS</given-names>
</name>
<name><surname>Brilliant</surname>
<given-names>L</given-names>
</name>
</person-group>
. <year>2009</year>
<article-title>Detecting influenza epidemics using search engine query data</article-title>
. <source>Nature</source>
<volume>457</volume>
, <fpage>1012</fpage>
–<lpage>1014</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1038/nature07634">doi:10.1038/nature07634</ext-link>
)</citation>
</ref>
<ref id="RSIF20140013C66"><label>66</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Betsch</surname>
<given-names>C</given-names>
</name>
<etal></etal>
</person-group>
<year>2012</year>
<article-title>Opportunities and challenges of Web 2.0 for vaccination decisions</article-title>
. <source>Vaccine</source>
<volume>30</volume>
, <fpage>3727</fpage>
–<lpage>3733</lpage>
. (<ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1016/j.vaccine.2012.02.025">doi:10.1016/j.vaccine.2012.02.025</ext-link>
)</citation>
</ref>
</ref-list>
</back>
</article>
</istex:document>
</istex:metadataXml>
<mods version="3.6"><titleInfo><title>A belief-based model for characterizing the spread of awareness and its impacts on individuals' vaccination decisions</title>
</titleInfo>
<titleInfo type="alternative" contentType="CDATA"><title>A belief-based model for characterizing the spread of awareness and its impacts on individuals' vaccination decisions</title>
</titleInfo>
<name type="personal"><namePart type="given">Shang</namePart>
<namePart type="family">Xia</namePart>
<affiliation>National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, People's Republic of China</affiliation>
<role><roleTerm type="text">author</roleTerm>
</role>
</name>
<name type="personal"><namePart type="given">Jiming</namePart>
<namePart type="family">Liu</namePart>
<affiliation>Department of Computer Science, Hong Kong Baptist University, Hong Kong</affiliation>
<affiliation>E-mail: jiming@comp.hkbu.edu.hk</affiliation>
<role><roleTerm type="text">author</roleTerm>
</role>
</name>
<typeOfResource>text</typeOfResource>
<genre type="article" displayLabel="research-article" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-6N5SZHKN-D">article</genre>
<originInfo><publisher>The Royal Society</publisher>
<dateIssued encoding="w3cdtf">2014-05-06</dateIssued>
<copyrightDate encoding="w3cdtf">2014</copyrightDate>
</originInfo>
<language><languageTerm type="code" authority="iso639-2b">eng</languageTerm>
<languageTerm type="code" authority="rfc3066">en</languageTerm>
</language>
<abstract>During an epidemic, individuals' decisions on whether or not to take vaccine may affect the dynamics of disease spread and, therefore, the effectiveness of disease control. Empirical studies have shown that such decisions can be subjected to individuals' awareness about disease and vaccine, such as their perceived disease severity and vaccine safety. The aim of this paper is to gain a better understanding of individuals' vaccination behaviour by modelling the spread of awareness in a group of socially connected individuals and examining the associated impacts on their vaccination decision-making. In our model, we examine whether or not individuals will get vaccinated as well as when they would. In doing so, we consider three possible decisions from an individual, i.e. to accept, to reject, and yet to decide, and further associate them with a set of belief values. Next, we extend the Dempster–Shafer theory to characterize individuals' belief value updates and their decision-making, having incorporated the awareness obtained from their connected neighbours. Furthermore, we examine two factors that will affect individuals' vaccination decisions: (i) reporting rates of disease- and vaccine-related events, and (ii) fading coefficient of awareness spread. By doing so, we can assess the impacts of awareness spread by evaluating the vaccination dynamics in terms of the number of vaccinated individuals. The results have demonstrated that the former influences the ratio of vaccinated individuals, whereas the latter affects the time when individuals decide to take vaccine.</abstract>
<subject><topic>vaccination decision</topic>
<topic>awareness</topic>
<topic>belief model</topic>
</subject>
<relatedItem type="host"><titleInfo><title>Journal of the Royal Society Interface</title>
</titleInfo>
<titleInfo type="abbreviated"><title>J. R. Soc. Interface</title>
</titleInfo>
<genre type="journal" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0GLKJH51-B">journal</genre>
<subject><genre>hwp-journal-coll</genre>
<topic>1004</topic>
<topic>44</topic>
</subject>
<identifier type="ISSN">1742-5689</identifier>
<identifier type="eISSN">1742-5662</identifier>
<identifier type="PublisherID">RSIF</identifier>
<identifier type="PublisherID-hwp">royinterface</identifier>
<part><date>2014</date>
<detail type="volume"><caption>vol.</caption>
<number>11</number>
</detail>
<detail type="issue"><caption>no.</caption>
<number>94</number>
</detail>
</part>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C1"><titleInfo><title>Directly transmitted infections diseases: control by vaccination</title>
</titleInfo>
<name type="personal"><namePart type="given">RM</namePart>
<namePart type="family">Anderson</namePart>
</name>
<name type="personal"><namePart type="given">RM</namePart>
<namePart type="family">May</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Science</title>
</titleInfo>
<part><date>1982</date>
<detail type="volume"><caption>vol.</caption>
<number>215</number>
</detail>
<extent unit="pages"><start>1053</start>
<end>1060</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1126/science.7063839</identifier>
<identifier type="doi">10.1126/science.7063839</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C2"><titleInfo><title>Vaccination and herd immunity to infectious diseases</title>
</titleInfo>
<name type="personal"><namePart type="given">RM</namePart>
<namePart type="family">Anderson</namePart>
</name>
<name type="personal"><namePart type="given">RM</namePart>
<namePart type="family">May</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Nature</title>
</titleInfo>
<part><date>1985</date>
<detail type="volume"><caption>vol.</caption>
<number>318</number>
</detail>
<extent unit="pages"><start>323</start>
<end>329</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1038/318323a0</identifier>
<identifier type="doi">10.1038/318323a0</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C3"><titleInfo><title>Strategies for mitigating an influenza pandemic</title>
</titleInfo>
<name type="personal"><namePart type="given">NM</namePart>
<namePart type="family">Ferguson</namePart>
</name>
<name type="personal"><namePart type="given">DAT</namePart>
<namePart type="family">Cummings</namePart>
</name>
<name type="personal"><namePart type="given">C</namePart>
<namePart type="family">Fraser</namePart>
</name>
<name type="personal"><namePart type="given">JC</namePart>
<namePart type="family">Cajka</namePart>
</name>
<name type="personal"><namePart type="given">PC</namePart>
<namePart type="family">Cooley</namePart>
</name>
<name type="personal"><namePart type="given">DS</namePart>
<namePart type="family">Burke</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Nature</title>
</titleInfo>
<part><date>2006</date>
<detail type="volume"><caption>vol.</caption>
<number>442</number>
</detail>
<extent unit="pages"><start>448</start>
<end>452</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1038/nature04795</identifier>
<identifier type="doi">10.1038/nature04795</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C4"><titleInfo><title>Containing pandemic influenza at the source</title>
</titleInfo>
<name type="personal"><namePart type="given">IM</namePart>
<namePart type="family">Longini</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Science</title>
</titleInfo>
<part><date>2005</date>
<detail type="volume"><caption>vol.</caption>
<number>309</number>
</detail>
<extent unit="pages"><start>1083</start>
<end>1087</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1126/science.1115717</identifier>
<identifier type="doi">10.1126/science.1115717</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C5"><titleInfo><title>Herd immunity and herd effect: new insights and definitions</title>
</titleInfo>
<name type="personal"><namePart type="given">TJ</namePart>
<namePart type="family">John</namePart>
</name>
<name type="personal"><namePart type="given">R</namePart>
<namePart type="family">Samuel</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Eur. J. Epidemiol.</title>
</titleInfo>
<part><date>2000</date>
<detail type="volume"><caption>vol.</caption>
<number>16</number>
</detail>
<extent unit="pages"><start>601</start>
<end>606</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1023/A:1007626510002</identifier>
<identifier type="doi">10.1023/A:1007626510002</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C6"><titleInfo><title>‘Herd immunity’: a rough guide</title>
</titleInfo>
<name type="personal"><namePart type="given">P</namePart>
<namePart type="family">Fine</namePart>
</name>
<name type="personal"><namePart type="given">K</namePart>
<namePart type="family">Eames</namePart>
</name>
<name type="personal"><namePart type="given">DL</namePart>
<namePart type="family">Heymann</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Clin. Infect. Dis.</title>
</titleInfo>
<part><date>2011</date>
<detail type="volume"><caption>vol.</caption>
<number>52</number>
</detail>
<extent unit="pages"><start>911</start>
<end>916</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1093/cid/cir007</identifier>
<identifier type="doi">10.1093/cid/cir007</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C7"><titleInfo><title>Addressing the vaccine confidence gap</title>
</titleInfo>
<name type="personal"><namePart type="given">HJ</namePart>
<namePart type="family">Larson</namePart>
</name>
<name type="personal"><namePart type="given">LZ</namePart>
<namePart type="family">Cooper</namePart>
</name>
<name type="personal"><namePart type="given">J</namePart>
<namePart type="family">Eskola</namePart>
</name>
<name type="personal"><namePart type="given">SL</namePart>
<namePart type="family">Katz</namePart>
</name>
<name type="personal"><namePart type="given">S</namePart>
<namePart type="family">Ratzan</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Lancet</title>
</titleInfo>
<part><date>2011</date>
<detail type="volume"><caption>vol.</caption>
<number>378</number>
</detail>
<extent unit="pages"><start>526</start>
<end>535</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1016/S0140-6736(11)60678-8</identifier>
<identifier type="doi">10.1016/S0140-6736(11)60678-8</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C8"><titleInfo><title>A crisis of public confidence in vaccines</title>
</titleInfo>
<name type="personal"><namePart type="given">S</namePart>
<namePart type="family">Black</namePart>
</name>
<name type="personal"><namePart type="given">R</namePart>
<namePart type="family">Rappuoli</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Sci. Transl. Med.</title>
</titleInfo>
<part><date>2010</date>
<detail type="volume"><caption>vol.</caption>
<number>2</number>
</detail>
<extent unit="pages"><start>61mr1</start>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1126/scitranslmed.3001738</identifier>
<identifier type="doi">10.1126/scitranslmed.3001738</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C9"><titleInfo><title>Determinants of adults intention to vaccinate against pandemic swine flu</title>
</titleInfo>
<name type="personal"><namePart type="given">LB</namePart>
<namePart type="family">Myers</namePart>
</name>
<name type="personal"><namePart type="given">R</namePart>
<namePart type="family">Goodwin</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>BMC Public Health</title>
</titleInfo>
<part><date>2011</date>
<detail type="volume"><caption>vol.</caption>
<number>11</number>
</detail>
<extent unit="pages"><start>11C15</start>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1186/1471-2458-11-15</identifier>
<identifier type="doi">10.1186/1471-2458-11-15</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C10"><titleInfo><title>Acceptance of pandemic (H1N1) 2009 influenza vaccination by the Australian public</title>
</titleInfo>
<name type="personal"><namePart type="given">K</namePart>
<namePart type="family">Eastwood</namePart>
</name>
<name type="personal"><namePart type="given">DN</namePart>
<namePart type="family">Durrheim</namePart>
</name>
<name type="personal"><namePart type="given">A</namePart>
<namePart type="family">Jones</namePart>
</name>
<name type="personal"><namePart type="given">M</namePart>
<namePart type="family">Butler</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Med. J. Aust.</title>
</titleInfo>
<part><date>2010</date>
<detail type="volume"><caption>vol.</caption>
<number>192</number>
</detail>
<extent unit="pages"><start>33</start>
<end>36</end>
</extent>
</part>
</relatedItem>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C11"><titleInfo><title>Factors affecting intention to receive and self-reported receipt of 2009 pandemic (H1N1) vaccine in Hong Kong: a longitudinal study</title>
</titleInfo>
<name type="personal"><namePart type="given">Q</namePart>
<namePart type="family">Liao</namePart>
</name>
<name type="personal"><namePart type="given">BJ</namePart>
<namePart type="family">Cowling</namePart>
</name>
<name type="personal"><namePart type="given">WWT</namePart>
<namePart type="family">Lam</namePart>
</name>
<name type="personal"><namePart type="given">R</namePart>
<namePart type="family">Fielding</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>PLoS ONE</title>
</titleInfo>
<part><date>2011</date>
<detail type="volume"><caption>vol.</caption>
<number>6</number>
</detail>
<extent unit="pages"><start>e17713</start>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1371/journal.pone.0017713</identifier>
<identifier type="doi">10.1371/journal.pone.0017713</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C12"><titleInfo><title>Public doubts about vaccination safety and resistance against vaccination</title>
</titleInfo>
<name type="personal"><namePart type="given">PH</namePart>
<namePart type="family">Streefland</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Health Policy</title>
</titleInfo>
<part><date>2001</date>
<detail type="volume"><caption>vol.</caption>
<number>55</number>
</detail>
<extent unit="pages"><start>159C172</start>
</extent>
</part>
</relatedItem>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C13"><titleInfo><title>Vaccine safety controversies and the future of vaccination programs</title>
</titleInfo>
<name type="personal"><namePart type="given">G</namePart>
<namePart type="family">Francois</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Pediatr. Infect. Dis. J.</title>
</titleInfo>
<part><date>2005</date>
<detail type="volume"><caption>vol.</caption>
<number>24</number>
</detail>
<extent unit="pages"><start>953</start>
<end>961</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1097/01.inf.0000183853.16113.a6</identifier>
<identifier type="doi">10.1097/01.inf.0000183853.16113.a6</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C14"><titleInfo><title>Vaccination and the theory of games</title>
</titleInfo>
<name type="personal"><namePart type="given">CT</namePart>
<namePart type="family">Bauch</namePart>
</name>
<name type="personal"><namePart type="given">DJD</namePart>
<namePart type="family">Earn</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Proc. Natl Acad. Sci. USA</title>
</titleInfo>
<part><date>2004</date>
<detail type="volume"><caption>vol.</caption>
<number>101</number>
</detail>
<extent unit="pages"><start>13 391</start>
<end>13 394</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1073/pnas.0403823101</identifier>
<identifier type="doi">10.1073/pnas.0403823101</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C15"><titleInfo><title>Acceptability of A/H1N1 vaccination during pandemic phase of influenza A/H1N1 in Hong Kong: population based cross sectional survey</title>
</titleInfo>
<name type="personal"><namePart type="given">JTF</namePart>
<namePart type="family">Lau</namePart>
</name>
<name type="personal"><namePart type="given">NCY</namePart>
<namePart type="family">Yeung</namePart>
</name>
<name type="personal"><namePart type="given">KC</namePart>
<namePart type="family">Choi</namePart>
</name>
<name type="personal"><namePart type="given">MY</namePart>
<namePart type="family">Cheng</namePart>
</name>
<name type="personal"><namePart type="given">HY</namePart>
<namePart type="family">Tsui</namePart>
</name>
<name type="personal"><namePart type="given">S</namePart>
<namePart type="family">Griffiths</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>BMJ</title>
</titleInfo>
<part><date>2009</date>
<detail type="volume"><caption>vol.</caption>
<number>339</number>
</detail>
<extent unit="pages"><start>b4164</start>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1136/bmj.b4164</identifier>
<identifier type="doi">10.1136/bmj.b4164</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C16"><titleInfo><title>Modelling the influence of human behaviour on the spread of infectious diseases: a review</title>
</titleInfo>
<name type="personal"><namePart type="given">S</namePart>
<namePart type="family">Funk</namePart>
</name>
<name type="personal"><namePart type="given">M</namePart>
<namePart type="family">Salathé</namePart>
</name>
<name type="personal"><namePart type="given">VAA</namePart>
<namePart type="family">Jansen</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>J. R. Soc. Interface</title>
</titleInfo>
<part><date>2010</date>
<detail type="volume"><caption>vol.</caption>
<number>7</number>
</detail>
<extent unit="pages"><start>1247</start>
<end>1256</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1098/rsif.2010.0142</identifier>
<identifier type="doi">10.1098/rsif.2010.0142</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C17"><titleInfo><title>Group interest versus self-interest in smallpox vaccination policy</title>
</titleInfo>
<name type="personal"><namePart type="given">CT</namePart>
<namePart type="family">Bauch</namePart>
</name>
<name type="personal"><namePart type="given">AP</namePart>
<namePart type="family">Galvani</namePart>
</name>
<name type="personal"><namePart type="given">DJD</namePart>
<namePart type="family">Earn</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Proc. Natl Acad. Sci. USA</title>
</titleInfo>
<part><date>2003</date>
<detail type="volume"><caption>vol.</caption>
<number>100</number>
</detail>
<extent unit="pages"><start>10 564</start>
<end>10 567</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1073/pnas.1731324100</identifier>
<identifier type="doi">10.1073/pnas.1731324100</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C18"><titleInfo><title>Social contact networks and disease eradicability under voluntary vaccination</title>
</titleInfo>
<name type="personal"><namePart type="given">A</namePart>
<namePart type="family">Perisic</namePart>
</name>
<name type="personal"><namePart type="given">CT</namePart>
<namePart type="family">Bauch</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>PLoS Comput. Biol.</title>
</titleInfo>
<part><date>2009</date>
<detail type="volume"><caption>vol.</caption>
<number>5</number>
</detail>
<extent unit="pages"><start>e1000280</start>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1371/journal.pcbi.1000280</identifier>
<identifier type="doi">10.1371/journal.pcbi.1000280</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C19"><titleInfo><title>A general approach for population games with application to vaccination</title>
</titleInfo>
<name type="personal"><namePart type="given">TC</namePart>
<namePart type="family">Reluga</namePart>
</name>
<name type="personal"><namePart type="given">AP</namePart>
<namePart type="family">Galvani</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Math. Biosci.</title>
</titleInfo>
<part><date>2011</date>
<detail type="volume"><caption>vol.</caption>
<number>230</number>
</detail>
<extent unit="pages"><start>67</start>
<end>78</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1016/j.mbs.2011.01.003</identifier>
<identifier type="doi">10.1016/j.mbs.2011.01.003</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C20"><titleInfo><title>Evolutionary game theory and social learning can determine how vaccine scares unfold</title>
</titleInfo>
<name type="personal"><namePart type="given">CT</namePart>
<namePart type="family">Bauch</namePart>
</name>
<name type="personal"><namePart type="given">S</namePart>
<namePart type="family">Bhattacharyya</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>PLoS Comput. Biol.</title>
</titleInfo>
<part><date>2012</date>
<detail type="volume"><caption>vol.</caption>
<number>8</number>
</detail>
<extent unit="pages"><start>e1002452</start>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1371/journal.pcbi.1002452</identifier>
<identifier type="doi">10.1371/journal.pcbi.1002452</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C21"><titleInfo><title>Imitation dynamics predict vaccinating behaviour</title>
</titleInfo>
<name type="personal"><namePart type="given">CT</namePart>
<namePart type="family">Bauch</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Proc. R Soc. B</title>
</titleInfo>
<part><date>2005</date>
<detail type="volume"><caption>vol.</caption>
<number>272</number>
</detail>
<extent unit="pages"><start>1669</start>
<end>1675</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1098/rspb.2005.3153</identifier>
<identifier type="doi">10.1098/rspb.2005.3153</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C22"><titleInfo><title>Imitation dynamics of vaccination behaviour on social networks</title>
</titleInfo>
<name type="personal"><namePart type="given">F</namePart>
<namePart type="family">Fu</namePart>
</name>
<name type="personal"><namePart type="given">DI</namePart>
<namePart type="family">Rosenbloom</namePart>
</name>
<name type="personal"><namePart type="given">L</namePart>
<namePart type="family">Wang</namePart>
</name>
<name type="personal"><namePart type="given">MA</namePart>
<namePart type="family">Nowak</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Proc. R. Soc. B</title>
</titleInfo>
<part><date>2011</date>
<detail type="volume"><caption>vol.</caption>
<number>278</number>
</detail>
<extent unit="pages"><start>42</start>
<end>49</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1098/rspb.2010.1107</identifier>
<identifier type="doi">10.1098/rspb.2010.1107</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C23"><titleInfo><title>The impact of vaccine side effects on the natural history of immunization programmes: an imitation-game approach</title>
</titleInfo>
<name type="personal"><namePart type="given">A</namePart>
<namePart type="family">d'Onofrio</namePart>
</name>
<name type="personal"><namePart type="given">P</namePart>
<namePart type="family">Manfredi</namePart>
</name>
<name type="personal"><namePart type="given">P</namePart>
<namePart type="family">Poletti</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>J. Theor. Biol.</title>
</titleInfo>
<part><date>2011</date>
<detail type="volume"><caption>vol.</caption>
<number>273</number>
</detail>
<extent unit="pages"><start>63</start>
<end>71</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1016/j.jtbi.2010.12.029</identifier>
<identifier type="doi">10.1016/j.jtbi.2010.12.029</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C24"><titleInfo><title>The impact of imitation on vaccination behavior in social contact networks</title>
</titleInfo>
<name type="personal"><namePart type="given">MLN</namePart>
<namePart type="family">Mbah</namePart>
</name>
<name type="personal"><namePart type="given">J</namePart>
<namePart type="family">Liu</namePart>
</name>
<name type="personal"><namePart type="given">CT</namePart>
<namePart type="family">Bauch</namePart>
</name>
<name type="personal"><namePart type="given">YI</namePart>
<namePart type="family">Tekel</namePart>
</name>
<name type="personal"><namePart type="given">J</namePart>
<namePart type="family">Medlock</namePart>
</name>
<name type="personal"><namePart type="given">LA</namePart>
<namePart type="family">Meyers</namePart>
</name>
<name type="personal"><namePart type="given">AP</namePart>
<namePart type="family">Galvani</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>PLoS Comput. Biol.</title>
</titleInfo>
<part><date>2012</date>
<detail type="volume"><caption>vol.</caption>
<number>8</number>
</detail>
<extent unit="pages"><start>e1002469</start>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1371/journal.pcbi.1002469</identifier>
<identifier type="doi">10.1371/journal.pcbi.1002469</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C25"><titleInfo><title>Dynamic modeling of vaccinating behavior as a function of individual beliefs</title>
</titleInfo>
<name type="personal"><namePart type="given">FC</namePart>
<namePart type="family">Coelho</namePart>
</name>
<name type="personal"><namePart type="given">CT</namePart>
<namePart type="family">Codeco</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>PLoS Comput. Biol.</title>
</titleInfo>
<part><date>2009</date>
<detail type="volume"><caption>vol.</caption>
<number>5</number>
</detail>
<extent unit="pages"><start>e1000425</start>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1371/journal.pcbi.1000425</identifier>
<identifier type="doi">10.1371/journal.pcbi.1000425</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C26"><titleInfo><title>Risk estimation of infectious diseases determines the effectiveness of the control strategy</title>
</titleInfo>
<name type="personal"><namePart type="given">H</namePart>
<namePart type="family">Zhang</namePart>
</name>
<name type="personal"><namePart type="given">J</namePart>
<namePart type="family">Zhang</namePart>
</name>
<name type="personal"><namePart type="given">P</namePart>
<namePart type="family">Li</namePart>
</name>
<name type="personal"><namePart type="given">M</namePart>
<namePart type="family">Small</namePart>
</name>
<name type="personal"><namePart type="given">B</namePart>
<namePart type="family">Wang</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Physica D</title>
</titleInfo>
<part><date>2011</date>
<detail type="volume"><caption>vol.</caption>
<number>240</number>
</detail>
<extent unit="pages"><start>943</start>
<end>948</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1016/j.physd.2011.02.001</identifier>
<identifier type="doi">10.1016/j.physd.2011.02.001</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C27"><titleInfo><title>Vaccinating behaviour, information, and the dynamics of SIR vaccine preventable diseases</title>
</titleInfo>
<name type="personal"><namePart type="given">A</namePart>
<namePart type="family">d'Onofrio</namePart>
</name>
<name type="personal"><namePart type="given">P</namePart>
<namePart type="family">Manfredi</namePart>
</name>
<name type="personal"><namePart type="given">E</namePart>
<namePart type="family">Salinelli</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Theor. Popul. Biol.</title>
</titleInfo>
<part><date>2007</date>
<detail type="volume"><caption>vol.</caption>
<number>71</number>
</detail>
<extent unit="pages"><start>301</start>
<end>317</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1016/j.tpb.2007.01.001</identifier>
<identifier type="doi">10.1016/j.tpb.2007.01.001</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C28"><titleInfo><title>Mean-field analysis of an inductive reasoning game: application to influenza vaccination</title>
</titleInfo>
<name type="personal"><namePart type="given">R</namePart>
<namePart type="family">Breban</namePart>
</name>
<name type="personal"><namePart type="given">R</namePart>
<namePart type="family">Vardavas</namePart>
</name>
<name type="personal"><namePart type="given">S</namePart>
<namePart type="family">Blower</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Phys. Rev. E</title>
</titleInfo>
<part><date>2007</date>
<detail type="volume"><caption>vol.</caption>
<number>76</number>
</detail>
<extent unit="pages"><start>031127</start>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1103/PhysRevE.76.031127</identifier>
<identifier type="doi">10.1103/PhysRevE.76.031127</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C29"><titleInfo><title>Vaccine demand driven by vaccine side effects: dynamic implications for SIR diseases</title>
</titleInfo>
<name type="personal"><namePart type="given">A</namePart>
<namePart type="family">d'Onofrio</namePart>
</name>
<name type="personal"><namePart type="given">P</namePart>
<namePart type="family">Manfredi</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>J. Theor. Biol.</title>
</titleInfo>
<part><date>2010</date>
<detail type="volume"><caption>vol.</caption>
<number>264</number>
</detail>
<extent unit="pages"><start>237</start>
<end>252</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1016/j.jtbi.2010.02.007</identifier>
<identifier type="doi">10.1016/j.jtbi.2010.02.007</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C30"><titleInfo><title>Seasonal infectious disease epidemiology</title>
</titleInfo>
<name type="personal"><namePart type="given">NC</namePart>
<namePart type="family">Grassly</namePart>
</name>
<name type="personal"><namePart type="given">C</namePart>
<namePart type="family">Fraser</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Proc. R. Soc. B</title>
</titleInfo>
<part><date>2006</date>
<detail type="volume"><caption>vol.</caption>
<number>273</number>
</detail>
<extent unit="pages"><start>2541</start>
<end>2550</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1098/rspb.2006.3604</identifier>
<identifier type="doi">10.1098/rspb.2006.3604</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C31"><titleInfo><title>The public's response to the 2009 H1N1 influenza pandemic</title>
</titleInfo>
<name type="personal"><namePart type="given">GK</namePart>
<namePart type="family">SteelFisher</namePart>
</name>
<name type="personal"><namePart type="given">RJ</namePart>
<namePart type="family">Blendon</namePart>
</name>
<name type="personal"><namePart type="given">MM</namePart>
<namePart type="family">Bekheit</namePart>
</name>
<name type="personal"><namePart type="given">K</namePart>
<namePart type="family">Lubell</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>N. Engl. J. Med.</title>
</titleInfo>
<part><date>2010</date>
<detail type="volume"><caption>vol.</caption>
<number>362</number>
</detail>
<extent unit="pages"><start>e65</start>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1056/NEJMp1005102</identifier>
<identifier type="doi">10.1056/NEJMp1005102</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C32"><titleInfo><title>Factors associated with uptake of vaccination against pandemic influenza: a systematic review</title>
</titleInfo>
<name type="personal"><namePart type="given">A</namePart>
<namePart type="family">Bish</namePart>
</name>
<name type="personal"><namePart type="given">L</namePart>
<namePart type="family">Yardley</namePart>
</name>
<name type="personal"><namePart type="given">A</namePart>
<namePart type="family">Nicoll</namePart>
</name>
<name type="personal"><namePart type="given">S</namePart>
<namePart type="family">Michie</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Vaccine</title>
</titleInfo>
<part><date>2011</date>
<detail type="volume"><caption>vol.</caption>
<number>29</number>
</detail>
<extent unit="pages"><start>6472</start>
<end>6484</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1016/j.vaccine.2011.06.107</identifier>
<identifier type="doi">10.1016/j.vaccine.2011.06.107</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C33"><titleInfo><title>The effect of epidemic measles on immunization rates</title>
</titleInfo>
<name type="personal"><namePart type="given">KP</namePart>
<namePart type="family">Goldstein</namePart>
</name>
<name type="personal"><namePart type="given">TJ</namePart>
<namePart type="family">Philipson</namePart>
</name>
<name type="personal"><namePart type="given">H</namePart>
<namePart type="family">Joo</namePart>
</name>
<name type="personal"><namePart type="given">RS</namePart>
<namePart type="family">Daum</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>JAMA</title>
</titleInfo>
<part><date>1996</date>
<detail type="volume"><caption>vol.</caption>
<number>276</number>
</detail>
<extent unit="pages"><start>56</start>
<end>58</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1001/jama.1996.03540010058031</identifier>
<identifier type="doi">10.1001/jama.1996.03540010058031</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C34"><titleInfo><title>Reasons for non-uptake of measles, mumps, and rubella catch up immunisation in a measles epidemic and side effects of the vaccine</title>
</titleInfo>
<name type="personal"><namePart type="given">RJ</namePart>
<namePart type="family">Roberts</namePart>
</name>
<name type="personal"><namePart type="given">QD</namePart>
<namePart type="family">Sandifer</namePart>
</name>
<name type="personal"><namePart type="given">MR</namePart>
<namePart type="family">Evans</namePart>
</name>
<name type="personal"><namePart type="given">MZ</namePart>
<namePart type="family">Nolan-Farrell</namePart>
</name>
<name type="personal"><namePart type="given">PM</namePart>
<namePart type="family">Davis</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>BMJ</title>
</titleInfo>
<part><date>1995</date>
<detail type="volume"><caption>vol.</caption>
<number>310</number>
</detail>
<extent unit="pages"><start>1629</start>
<end>1632</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1136/bmj.310.6995.1629</identifier>
<identifier type="doi">10.1136/bmj.310.6995.1629</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C35"><titleInfo><title>Another media scare about MMR vaccine hits Britain</title>
</titleInfo>
<name type="personal"><namePart type="given">P</namePart>
<namePart type="family">Anderson</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>BMJ</title>
</titleInfo>
<part><date>1999</date>
<detail type="volume"><caption>vol.</caption>
<number>318</number>
</detail>
<extent unit="pages"><start>1578</start>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1136/bmj.318.7198.1578</identifier>
<identifier type="doi">10.1136/bmj.318.7198.1578</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C36"><titleInfo><title>Medicine in the popular press: the influence of the media on perceptions of disease</title>
</titleInfo>
<name type="personal"><namePart type="given">ME</namePart>
<namePart type="family">Young</namePart>
</name>
<name type="personal"><namePart type="given">GR</namePart>
<namePart type="family">Norman</namePart>
</name>
<name type="personal"><namePart type="given">KR</namePart>
<namePart type="family">Humphreys</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>PLoS ONE</title>
</titleInfo>
<part><date>2008</date>
<detail type="volume"><caption>vol.</caption>
<number>3</number>
</detail>
<extent unit="pages"><start>e3552</start>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1371/journal.pone.0003552</identifier>
<identifier type="doi">10.1371/journal.pone.0003552</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C37"><titleInfo><title>Assessing vaccination sentiments with online social media: implications for infectious disease dynamics and control</title>
</titleInfo>
<name type="personal"><namePart type="given">M</namePart>
<namePart type="family">Salathe</namePart>
</name>
<name type="personal"><namePart type="given">S</namePart>
<namePart type="family">Khandelwal</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>PLoS Comput. Biol.</title>
</titleInfo>
<part><date>2011</date>
<detail type="volume"><caption>vol.</caption>
<number>7</number>
</detail>
<extent unit="pages"><start>e1002199</start>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1371/journal.pcbi.1002199</identifier>
<identifier type="doi">10.1371/journal.pcbi.1002199</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C38"><titleInfo><title>Social internet sites as a source of public health information</title>
</titleInfo>
<name type="personal"><namePart type="given">K</namePart>
<namePart type="family">Vance</namePart>
</name>
<name type="personal"><namePart type="given">W</namePart>
<namePart type="family">Howe</namePart>
</name>
<name type="personal"><namePart type="given">RP</namePart>
<namePart type="family">Dellavalle</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Dermatol. Clin.</title>
</titleInfo>
<part><date>2009</date>
<detail type="volume"><caption>vol.</caption>
<number>27</number>
</detail>
<extent unit="pages"><start>133</start>
<end>136</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1016/j.det.2008.11.010</identifier>
<identifier type="doi">10.1016/j.det.2008.11.010</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C39"><titleInfo><title>The use of Twitter to track levels of disease activity and public concern in the US during the influenza A H1N1 pandemic</title>
</titleInfo>
<name type="personal"><namePart type="given">A</namePart>
<namePart type="family">Signorini</namePart>
</name>
<name type="personal"><namePart type="given">AM</namePart>
<namePart type="family">Segre</namePart>
</name>
<name type="personal"><namePart type="given">PM</namePart>
<namePart type="family">Polgreen</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>PLoS ONE</title>
</titleInfo>
<part><date>2011</date>
<detail type="volume"><caption>vol.</caption>
<number>6</number>
</detail>
<extent unit="pages"><start>e19467</start>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1371/journal.pone.0019467</identifier>
<identifier type="doi">10.1371/journal.pone.0019467</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C40"><titleInfo><title>YouTube as a source of information on immunization: a content analysis</title>
</titleInfo>
<name type="personal"><namePart type="given">J</namePart>
<namePart type="family">Keelan</namePart>
</name>
<name type="personal"><namePart type="given">V</namePart>
<namePart type="family">Pavri-Garcia</namePart>
</name>
<name type="personal"><namePart type="given">G</namePart>
<namePart type="family">Tomlinson</namePart>
</name>
<name type="personal"><namePart type="given">K</namePart>
<namePart type="family">Wilson</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>JAMA</title>
</titleInfo>
<part><date>2007</date>
<detail type="volume"><caption>vol.</caption>
<number>298</number>
</detail>
<extent unit="pages"><start>2482</start>
<end>2484</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1001/jama.298.21.2482</identifier>
<identifier type="doi">10.1001/jama.298.21.2482</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C41"><titleInfo><title>YouTube as a source of information on the H1N1 influenza pandemic</title>
</titleInfo>
<name type="personal"><namePart type="given">A</namePart>
<namePart type="family">Pandey</namePart>
</name>
<name type="personal"><namePart type="given">N</namePart>
<namePart type="family">Patni</namePart>
</name>
<name type="personal"><namePart type="given">M</namePart>
<namePart type="family">Singh</namePart>
</name>
<name type="personal"><namePart type="given">A</namePart>
<namePart type="family">Sood</namePart>
</name>
<name type="personal"><namePart type="given">G</namePart>
<namePart type="family">Singh</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Am. J. Prev. Med.</title>
</titleInfo>
<part><date>2010</date>
<detail type="volume"><caption>vol.</caption>
<number>38</number>
</detail>
<extent unit="pages"><start>e1</start>
<end>e3</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1016/j.amepre.2009.11.007</identifier>
<identifier type="doi">10.1016/j.amepre.2009.11.007</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C42"><titleInfo><title>The defining characteristics of Web 2.0 and their potential influence in the online vaccination debate</title>
</titleInfo>
<name type="personal"><namePart type="given">HO</namePart>
<namePart type="family">Witteman</namePart>
</name>
<name type="personal"><namePart type="given">BJ</namePart>
<namePart type="family">Zikmund-Fisher</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Vaccine</title>
</titleInfo>
<part><date>2011</date>
<detail type="volume"><caption>vol.</caption>
<number>30</number>
</detail>
<extent unit="pages"><start>3734</start>
<end>3740</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1016/j.vaccine.2011.12.039</identifier>
<identifier type="doi">10.1016/j.vaccine.2011.12.039</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C43"><titleInfo><title>An analysis of the human papilloma virus vaccine debate on MySpace blogs</title>
</titleInfo>
<name type="personal"><namePart type="given">J</namePart>
<namePart type="family">Keelan</namePart>
</name>
<name type="personal"><namePart type="given">V</namePart>
<namePart type="family">Pavri</namePart>
</name>
<name type="personal"><namePart type="given">R</namePart>
<namePart type="family">Balakrishnan</namePart>
</name>
<name type="personal"><namePart type="given">K</namePart>
<namePart type="family">Wilson</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Vaccine</title>
</titleInfo>
<part><date>2010</date>
<detail type="volume"><caption>vol.</caption>
<number>28</number>
</detail>
<extent unit="pages"><start>1535</start>
<end>1540</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1016/j.vaccine.2009.11.060</identifier>
<identifier type="doi">10.1016/j.vaccine.2009.11.060</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C44"><titleInfo><title>What the public was saying about the H1N1 vaccine: perceptions and issues discussed in on-line comments during the 2009 H1N1 pandemic</title>
</titleInfo>
<name type="personal"><namePart type="given">N</namePart>
<namePart type="family">Henrich</namePart>
</name>
<name type="personal"><namePart type="given">B</namePart>
<namePart type="family">Holmes</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>PLoS ONE</title>
</titleInfo>
<part><date>2011</date>
<detail type="volume"><caption>vol.</caption>
<number>6</number>
</detail>
<extent unit="pages"><start>e18479</start>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1371/journal.pone.0018479</identifier>
<identifier type="doi">10.1371/journal.pone.0018479</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C45"><titleInfo><title>A mathematical theory of evidence</title>
</titleInfo>
<name type="personal"><namePart type="given">G</namePart>
<namePart type="family">Shafer</namePart>
</name>
<originInfo><publisher>Royal Society of London</publisher>
<publisher>Princeton University Press. </publisher>
<place><placeTerm type="text">Princeton, NJ</placeTerm>
</place>
</originInfo>
<genre>book</genre>
<part><date>1976</date>
<detail type="volume"><caption>vol.</caption>
<number>vol. 76</number>
</detail>
</part>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C46"><titleInfo><title>Upper and lower probabilities induced by a multivalued mapping</title>
</titleInfo>
<name type="personal"><namePart type="given">AP</namePart>
<namePart type="family">Dempster</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Ann. Math. Stat.</title>
</titleInfo>
<part><date>1967</date>
<detail type="volume"><caption>vol.</caption>
<number>38</number>
</detail>
<extent unit="pages"><start>325</start>
<end>339</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1214/aoms/1177698950</identifier>
<identifier type="doi">10.1214/aoms/1177698950</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C47"><titleInfo><title>The spread of awareness and its impact on epidemic outbreaks</title>
</titleInfo>
<name type="personal"><namePart type="given">S</namePart>
<namePart type="family">Funk</namePart>
</name>
<name type="personal"><namePart type="given">E</namePart>
<namePart type="family">Gilada</namePart>
</name>
<name type="personal"><namePart type="given">C</namePart>
<namePart type="family">Watkinsb</namePart>
</name>
<name type="personal"><namePart type="given">VAA</namePart>
<namePart type="family">Jansena</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Proc. Natl Acad. Sci. USA</title>
</titleInfo>
<part><date>2009</date>
<detail type="volume"><caption>vol.</caption>
<number>106</number>
</detail>
<extent unit="pages"><start>6872</start>
<end>6877</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1073/pnas.0810762106</identifier>
<identifier type="doi">10.1073/pnas.0810762106</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C48"><titleInfo><title>Combination of evidence in Dempster–Shafer theory</title>
</titleInfo>
<name type="personal"><namePart type="given">K</namePart>
<namePart type="family">Sentz</namePart>
</name>
<name type="personal"><namePart type="given">S</namePart>
<namePart type="family">Ferson</namePart>
</name>
<originInfo><publisher>Royal Society of London</publisher>
<publisher>Sandia National Laboratories. </publisher>
<place><placeTerm type="text">Albuquerque, NM</placeTerm>
</place>
</originInfo>
<genre>book</genre>
<part><date>2002</date>
</part>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C49"><titleInfo><title>The effective reproduction number of pandemic influenza: prospective estimation</title>
</titleInfo>
<name type="personal"><namePart type="given">BJ</namePart>
<namePart type="family">Cowling</namePart>
</name>
<name type="personal"><namePart type="given">MSY</namePart>
<namePart type="family">Lau</namePart>
</name>
<name type="personal"><namePart type="given">LM</namePart>
<namePart type="family">Ho</namePart>
</name>
<name type="personal"><namePart type="given">SK</namePart>
<namePart type="family">Chuang</namePart>
</name>
<name type="personal"><namePart type="given">T</namePart>
<namePart type="family">Tsang</namePart>
</name>
<name type="personal"><namePart type="given">SH</namePart>
<namePart type="family">Liu</namePart>
</name>
<name type="personal"><namePart type="given">P-Y</namePart>
<namePart type="family">Leung</namePart>
</name>
<name type="personal"><namePart type="given">S-V</namePart>
<namePart type="family">Lo</namePart>
</name>
<name type="personal"><namePart type="given">EHY</namePart>
<namePart type="family">Lau</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Epidemiology</title>
</titleInfo>
<part><date>2010</date>
<detail type="volume"><caption>vol.</caption>
<number>21</number>
</detail>
<extent unit="pages"><start>842</start>
<end>846</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1097/EDE.0b013e3181f20977</identifier>
<identifier type="doi">10.1097/EDE.0b013e3181f20977</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C50"><titleInfo><title>Estimating infection attack rates and severity in real time during an influenza pandemic: analysis of serial cross-sectional serologic surveillance data</title>
</titleInfo>
<name type="personal"><namePart type="given">JT</namePart>
<namePart type="family">Wu</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>PLoS Med.</title>
</titleInfo>
<part><date>2011</date>
<detail type="volume"><caption>vol.</caption>
<number>8</number>
</detail>
<extent unit="pages"><start>e1001103</start>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1371/journal.pmed.1001103</identifier>
<identifier type="doi">10.1371/journal.pmed.1001103</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C51"><titleInfo><title>The infection attack rate and severity of 2009 pandemic H1N1 influenza in Hong Kong</title>
</titleInfo>
<name type="personal"><namePart type="given">JT</namePart>
<namePart type="family">Wu</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Clin. Infect. Dis.</title>
</titleInfo>
<part><date>2010</date>
<detail type="volume"><caption>vol.</caption>
<number>51</number>
</detail>
<extent unit="pages"><start>1184</start>
<end>1191</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1086/656740</identifier>
<identifier type="doi">10.1086/656740</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C52"><titleInfo><title>Information Services Department of the Hong Kong Government. Human swine influenza vaccination programme. See http://www.info.gov.hk/gia/general/201003/17/P201003170168.htm.</title>
</titleInfo>
<name type="corporate"><namePart>Information Services Department of the Hong Kong Government</namePart>
</name>
<genre>other</genre>
<note>Human swine influenza vaccination programme</note>
<note>Information Services Department of the Hong Kong Government. Human swine influenza vaccination programme. See http://www.info.gov.hk/gia/general/201003/17/P201003170168.htm.</note>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C53"><titleInfo><title>Centre for Health Protection. Summary report on the surveillance of adverse events following HSI immunisation and expert groups comment on the safety of HSI vaccine in Hong Kong;. September 2010. Online. See http://www.chp.gov.hk/files/pdf/hsi_vaccine_aefi_report_en.pdf.</title>
</titleInfo>
<name type="corporate"><namePart>Centre for Health Protection</namePart>
</name>
<genre>other</genre>
<note>Summary report on the surveillance of adverse events following HSI immunisation and expert groups comment on the safety of HSI vaccine in Hong Kong;. September 2010. Online</note>
<note>Centre for Health Protection. Summary report on the surveillance of adverse events following HSI immunisation and expert groups comment on the safety of HSI vaccine in Hong Kong;. September 2010. Online. See http://www.chp.gov.hk/files/pdf/hsi_vaccine_aefi_report_en.pdf.</note>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C54"><titleInfo><title>Patterns and dynamics of users’ behavior and interaction: network analysis of an online community</title>
</titleInfo>
<name type="personal"><namePart type="given">P</namePart>
<namePart type="family">Panzarasa</namePart>
</name>
<name type="personal"><namePart type="given">T</namePart>
<namePart type="family">Opsahl</namePart>
</name>
<name type="personal"><namePart type="given">KM</namePart>
<namePart type="family">Carley</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>J. Am. Soc. Inf. Sci. Technol.</title>
</titleInfo>
<part><date>2009</date>
<detail type="volume"><caption>vol.</caption>
<number>60</number>
</detail>
<extent unit="pages"><start>911</start>
<end>932</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1002/asi.v60:5</identifier>
<identifier type="doi">10.1002/asi.v60:5</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C55"><titleInfo><title>Adaptive human behavior in epidemiological models</title>
</titleInfo>
<name type="personal"><namePart type="given">EP</namePart>
<namePart type="family">Fenichel</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Proc. Natl Acad. Sci. USA</title>
</titleInfo>
<part><date>2011</date>
<detail type="volume"><caption>vol.</caption>
<number>108</number>
</detail>
<extent unit="pages"><start>6306</start>
<end>6311</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1073/pnas.1011250108</identifier>
<identifier type="doi">10.1073/pnas.1011250108</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C56"><titleInfo><title>Factors in association with acceptability of A/H1N1 vaccination during the influenza A/H1N1 pandemic phase in the Hong Kong general population</title>
</titleInfo>
<name type="personal"><namePart type="given">JTF</namePart>
<namePart type="family">Lau</namePart>
</name>
<name type="personal"><namePart type="given">NCY</namePart>
<namePart type="family">Yeung</namePart>
</name>
<name type="personal"><namePart type="given">MYMC</namePart>
<namePart type="family">Choi</namePart>
</name>
<name type="personal"><namePart type="given">HY</namePart>
<namePart type="family">Tsui</namePart>
</name>
<name type="personal"><namePart type="given">S</namePart>
<namePart type="family">Griffiths</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Vaccine</title>
</titleInfo>
<part><date>2010</date>
<detail type="volume"><caption>vol.</caption>
<number>28</number>
</detail>
<extent unit="pages"><start>4632C4637</start>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1016/j.vaccine.2010.04.076</identifier>
<identifier type="doi">10.1016/j.vaccine.2010.04.076</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C57"><titleInfo><title>Why do I need it? I am not at risk! Public perceptions towards the pandemic (H1N1) 2009 vaccine</title>
</titleInfo>
<name type="personal"><namePart type="given">H</namePart>
<namePart type="family">Seale</namePart>
</name>
<name type="personal"><namePart type="given">AE</namePart>
<namePart type="family">Heywood</namePart>
</name>
<name type="personal"><namePart type="given">ML</namePart>
<namePart type="family">McLaws</namePart>
</name>
<name type="personal"><namePart type="given">KF</namePart>
<namePart type="family">Ward</namePart>
</name>
<name type="personal"><namePart type="given">CP</namePart>
<namePart type="family">Lowbridge</namePart>
</name>
<name type="personal"><namePart type="given">D</namePart>
<namePart type="family">Van</namePart>
</name>
<name type="personal"><namePart type="given">CR</namePart>
<namePart type="family">MacIntyre</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>BMC Infect. Dis.</title>
</titleInfo>
<part><date>2010</date>
<detail type="volume"><caption>vol.</caption>
<number>10</number>
</detail>
<extent unit="pages"><start>99</start>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1186/1471-2334-10-99</identifier>
<identifier type="doi">10.1186/1471-2334-10-99</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C58"><titleInfo><title>Which factors are important in adults uptake of a (pre)pandemic influenza vaccine?</title>
</titleInfo>
<name type="personal"><namePart type="given">EAM</namePart>
<namePart type="family">Zijtregtop</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Vaccine</title>
</titleInfo>
<part><date>2009</date>
<detail type="volume"><caption>vol.</caption>
<number>28</number>
</detail>
<extent unit="pages"><start>207</start>
<end>227</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1016/j.vaccine.2009.09.099</identifier>
<identifier type="doi">10.1016/j.vaccine.2009.09.099</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C59"><titleInfo><title>Factors influencing the uptake of 2009 H1N1 influenza vaccine in a multiethnic Asian population</title>
</titleInfo>
<name type="personal"><namePart type="given">LP</namePart>
<namePart type="family">Wong</namePart>
</name>
<name type="personal"><namePart type="given">IC</namePart>
<namePart type="family">Samb</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Vaccine</title>
</titleInfo>
<part><date>2010</date>
<detail type="volume"><caption>vol.</caption>
<number>28</number>
</detail>
<extent unit="pages"><start>4499</start>
<end>4505</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1016/j.vaccine.2010.04.043</identifier>
<identifier type="doi">10.1016/j.vaccine.2010.04.043</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C60"><titleInfo><title>Towards a characterization of behavior-disease models</title>
</titleInfo>
<name type="personal"><namePart type="given">N</namePart>
<namePart type="family">Perra</namePart>
</name>
<name type="personal"><namePart type="given">D</namePart>
<namePart type="family">Balcan</namePart>
</name>
<name type="personal"><namePart type="given">B</namePart>
<namePart type="family">Goncalves</namePart>
</name>
<name type="personal"><namePart type="given">A</namePart>
<namePart type="family">Vespignani</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>PLoS ONE</title>
</titleInfo>
<part><date>2011</date>
<detail type="volume"><caption>vol.</caption>
<number>6</number>
</detail>
<extent unit="pages"><start>e23084</start>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1371/journal.pone.0023084</identifier>
<identifier type="doi">10.1371/journal.pone.0023084</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C61"><titleInfo><title>Vaccines for the unvaccinated: protecting the herd</title>
</titleInfo>
<name type="personal"><namePart type="given">DS</namePart>
<namePart type="family">Stephens</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>J Infect. Dis.</title>
</titleInfo>
<part><date>2008</date>
<detail type="volume"><caption>vol.</caption>
<number>197</number>
</detail>
<extent unit="pages"><start>643</start>
<end>645</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1086/527402</identifier>
<identifier type="doi">10.1086/527402</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C62"><titleInfo><title>Dynamic equilibria of group vaccination strategies in a heterogeneous population</title>
</titleInfo>
<name type="personal"><namePart type="given">MG</namePart>
<namePart type="family">Cojocaru</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>J. Glob. Optim.</title>
</titleInfo>
<part><date>2008</date>
<detail type="volume"><caption>vol.</caption>
<number>40</number>
</detail>
<extent unit="pages"><start>51C63</start>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1007/s10898-007-9204-7</identifier>
<identifier type="doi">10.1007/s10898-007-9204-7</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C63"><titleInfo><title>A simulation analysis to characterize the dynamics of vaccinating behaviour on contact networks</title>
</titleInfo>
<name type="personal"><namePart type="given">A</namePart>
<namePart type="family">Perisic</namePart>
</name>
<name type="personal"><namePart type="given">CT</namePart>
<namePart type="family">Bauch</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>BMC Infect. Dis.</title>
</titleInfo>
<part><date>2009</date>
<detail type="volume"><caption>vol.</caption>
<number>9</number>
</detail>
<extent unit="pages"><start>77</start>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1186/1471-2334-9-77</identifier>
<identifier type="doi">10.1186/1471-2334-9-77</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C64"><titleInfo><title>Users of the world, unite! The challenges and opportunities of social media</title>
</titleInfo>
<name type="personal"><namePart type="given">AM</namePart>
<namePart type="family">Kaplan</namePart>
</name>
<name type="personal"><namePart type="given">M</namePart>
<namePart type="family">Haenlein</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Bus. Horiz.</title>
</titleInfo>
<part><date>2010</date>
<detail type="volume"><caption>vol.</caption>
<number>53</number>
</detail>
<extent unit="pages"><start>59</start>
<end>68</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1016/j.bushor.2009.09.003</identifier>
<identifier type="doi">10.1016/j.bushor.2009.09.003</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C65"><titleInfo><title>Detecting influenza epidemics using search engine query data</title>
</titleInfo>
<name type="personal"><namePart type="given">J</namePart>
<namePart type="family">Ginsberg</namePart>
</name>
<name type="personal"><namePart type="given">MH</namePart>
<namePart type="family">Mohebbi</namePart>
</name>
<name type="personal"><namePart type="given">RS</namePart>
<namePart type="family">Patel</namePart>
</name>
<name type="personal"><namePart type="given">L</namePart>
<namePart type="family">Brammer</namePart>
</name>
<name type="personal"><namePart type="given">MS</namePart>
<namePart type="family">Smolinski</namePart>
</name>
<name type="personal"><namePart type="given">L</namePart>
<namePart type="family">Brilliant</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Nature</title>
</titleInfo>
<part><date>2009</date>
<detail type="volume"><caption>vol.</caption>
<number>457</number>
</detail>
<extent unit="pages"><start>1012</start>
<end>1014</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1038/nature07634</identifier>
<identifier type="doi">10.1038/nature07634</identifier>
</relatedItem>
<relatedItem type="references" displayLabel="RSIF20140013C66"><titleInfo><title>Opportunities and challenges of Web 2.0 for vaccination decisions</title>
</titleInfo>
<name type="personal"><namePart type="given">C</namePart>
<namePart type="family">Betsch</namePart>
</name>
<genre>journal</genre>
<relatedItem type="host"><titleInfo><title>Vaccine</title>
</titleInfo>
<part><date>2012</date>
<detail type="volume"><caption>vol.</caption>
<number>30</number>
</detail>
<extent unit="pages"><start>3727</start>
<end>3733</end>
</extent>
</part>
</relatedItem>
<identifier type="uri">http://dx.doi.org/10.1016/j.vaccine.2012.02.025</identifier>
<identifier type="doi">10.1016/j.vaccine.2012.02.025</identifier>
</relatedItem>
<identifier type="istex">E68119CAA8C74DBCAABE30AF45939C204F2E45A2</identifier>
<identifier type="ark">ark:/67375/V84-9XMKW6D1-P</identifier>
<identifier type="DOI">10.1098/rsif.2014.0013</identifier>
<identifier type="href">rsif20140013.pdf</identifier>
<identifier type="ArticleID">rsif20140013</identifier>
<accessCondition type="use and reproduction" contentType="open-access">© 2014 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original author and source are credited.</accessCondition>
<recordInfo><recordContentSource authority="ISTEX" authorityURI="https://loaded-corpus.data.istex.fr" valueURI="https://loaded-corpus.data.istex.fr/ark:/67375/XBH-W19DTZ70-2">RSL</recordContentSource>
<recordOrigin>Converted from (version 1.2.10) to MODS version 3.6.</recordOrigin>
<recordCreationDate encoding="w3cdtf">2020-04-30</recordCreationDate>
</recordInfo>
</mods>
<json:item><extension>json</extension>
<original>false</original>
<mimetype>application/json</mimetype>
<uri>https://api.istex.fr/ark:/67375/V84-9XMKW6D1-P/record.json</uri>
</json:item>
</metadata>
<annexes><json:item><extension>tiff</extension>
<original>true</original>
<mimetype>image/tiff</mimetype>
<uri>https://api.istex.fr/document/E68119CAA8C74DBCAABE30AF45939C204F2E45A2/annexes/tiff</uri>
</json:item>
<json:item><extension>jpeg</extension>
<original>true</original>
<mimetype>image/jpeg</mimetype>
<uri>https://api.istex.fr/ark:/67375/V84-9XMKW6D1-P/annexes.jpeg</uri>
</json:item>
<json:item><extension>gif</extension>
<original>true</original>
<mimetype>image/gif</mimetype>
<uri>https://api.istex.fr/ark:/67375/V84-9XMKW6D1-P/annexes.gif</uri>
</json:item>
</annexes>
<serie></serie>
</istex>
</record>
Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Sante/explor/PandemieGrippaleV1/Data/Istex/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000A35 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Istex/Corpus/biblio.hfd -nk 000A35 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien |wiki= Sante |area= PandemieGrippaleV1 |flux= Istex |étape= Corpus |type= RBID |clé= ISTEX:E68119CAA8C74DBCAABE30AF45939C204F2E45A2 |texte= A belief-based model for characterizing the spread of awareness and its impacts on individuals' vaccination decisions }}
![]() | This area was generated with Dilib version V0.6.34. | ![]() |