Modeling epidemic spread with awareness and heterogeneous transmission rates in networks
Identifieur interne : 001A85 ( Main/Exploration ); précédent : 001A84; suivant : 001A86Modeling epidemic spread with awareness and heterogeneous transmission rates in networks
Auteurs : Yilun Shang [États-Unis]Source :
- Journal of Biological Physics [ 0092-0606 ] ; 2013-06-01.
English descriptors
Abstract
Abstract: During an epidemic outbreak in a human population, susceptibility to infection can be reduced by raising awareness of the disease. In this paper, we investigate the effects of three forms of awareness (i.e., contact, local, and global) on the spread of a disease in a random network. Connectivity-correlated transmission rates are assumed. By using the mean-field theory and numerical simulation, we show that both local and contact awareness can raise the epidemic thresholds while the global awareness cannot, which mirrors the recent results of Wu et al. The obtained results point out that individual behaviors in the presence of an infectious disease has a great influence on the epidemic dynamics. Our method enriches mean-field analysis in epidemic models.
Url:
DOI: 10.1007/s10867-013-9318-8
Affiliations:
Links toward previous steps (curation, corpus...)
- to stream Istex, to step Corpus: 001646
- to stream Istex, to step Curation: 001646
- to stream Istex, to step Checkpoint: 000363
- to stream Main, to step Merge: 001A95
- to stream Main, to step Curation: 001A85
Le document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Modeling epidemic spread with awareness and heterogeneous transmission rates in networks</title><author><name sortKey="Shang, Yilun" sort="Shang, Yilun" uniqKey="Shang Y" first="Yilun" last="Shang">Yilun Shang</name></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:8F9EB7BA7C47B2FA63875ABFEABEE53A4BE3E698</idno><date when="2013" year="2013">2013</date><idno type="doi">10.1007/s10867-013-9318-8</idno><idno type="url">https://api.istex.fr/ark:/67375/VQC-GWG1C6S2-C/fulltext.pdf</idno><idno type="wicri:Area/Istex/Corpus">001646</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">001646</idno><idno type="wicri:Area/Istex/Curation">001646</idno><idno type="wicri:Area/Istex/Checkpoint">000363</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Checkpoint">000363</idno><idno type="wicri:doubleKey">0092-0606:2013:Shang Y:modeling:epidemic:spread</idno><idno type="wicri:Area/Main/Merge">001A95</idno><idno type="wicri:Area/Main/Curation">001A85</idno><idno type="wicri:Area/Main/Exploration">001A85</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Modeling epidemic spread with awareness and heterogeneous transmission rates in networks</title><author><name sortKey="Shang, Yilun" sort="Shang, Yilun" uniqKey="Shang Y" first="Yilun" last="Shang">Yilun Shang</name><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Institute for Cyber Security, University of Texas at San Antonio, 78249, San Antonio, TX</wicri:regionArea><placeName><region type="state">Texas</region></placeName></affiliation><affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Journal of Biological Physics</title><title level="j" type="abbrev">J Biol Phys</title><idno type="ISSN">0092-0606</idno><idno type="eISSN">1573-0689</idno><imprint><publisher>Springer Netherlands</publisher><pubPlace>Dordrecht</pubPlace><date type="published" when="2013-06-01">2013-06-01</date><biblScope unit="volume">39</biblScope><biblScope unit="issue">3</biblScope><biblScope unit="page" from="489">489</biblScope><biblScope unit="page" to="500">500</biblScope></imprint><idno type="ISSN">0092-0606</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0092-0606</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Behavioral response</term><term>Complex network</term><term>Epidemic spread</term><term>Heterogeneous transmission</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: During an epidemic outbreak in a human population, susceptibility to infection can be reduced by raising awareness of the disease. In this paper, we investigate the effects of three forms of awareness (i.e., contact, local, and global) on the spread of a disease in a random network. Connectivity-correlated transmission rates are assumed. By using the mean-field theory and numerical simulation, we show that both local and contact awareness can raise the epidemic thresholds while the global awareness cannot, which mirrors the recent results of Wu et al. The obtained results point out that individual behaviors in the presence of an infectious disease has a great influence on the epidemic dynamics. Our method enriches mean-field analysis in epidemic models.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Texas</li></region></list><tree><country name="États-Unis"><region name="Texas"><name sortKey="Shang, Yilun" sort="Shang, Yilun" uniqKey="Shang Y" first="Yilun" last="Shang">Yilun Shang</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Sante/explor/SrasV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 001A85 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 001A85 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Sante
|area= SrasV1
|flux= Main
|étape= Exploration
|type= RBID
|clé= ISTEX:8F9EB7BA7C47B2FA63875ABFEABEE53A4BE3E698
|texte= Modeling epidemic spread with awareness and heterogeneous transmission rates in networks
}}
| This area was generated with Dilib version V0.6.33. |  |