PandemieGrippaleV1, PubMed, Corpus, bibRecord, 000694

A Bayesian inferential approach to quantify the transmission intensity of disease outbreak.

Identifieur interne : 000694 ( PubMed/Corpus ); précédent : 000693; suivant : 000695

A Bayesian inferential approach to quantify the transmission intensity of disease outbreak.

Auteurs : Adiveppa S. Kadi ; Shivakumari R. Avaradi

Source :

Computational and mathematical methods in medicine [ 1748-6718 ] ; 2015.

RBID : pubmed:25784956

English descriptors

KwdEn :
- Algorithms, Basic Reproduction Number, Bayes Theorem, Disease Outbreaks, Humans, India, Influenza A Virus, H1N1 Subtype, Influenza, Human (epidemiology), Influenza, Human (transmission), Models, Statistical, Probability, Software.
MESH :
- geographic : India.
- epidemiology : Influenza, Human.
- transmission : Influenza, Human.
- Algorithms, Basic Reproduction Number, Bayes Theorem, Disease Outbreaks, Humans, Influenza A Virus, H1N1 Subtype, Models, Statistical, Probability, Software.

Abstract

Emergence of infectious diseases like influenza pandemic (H1N1) 2009 has become great concern, which posed new challenges to the health authorities worldwide. To control these diseases various studies have been developed in the field of mathematical modelling, which is useful tool for understanding the epidemiological dynamics and their dependence on social mixing patterns.

DOI: 10.1155/2015/256319
PubMed: 25784956

Links to Exploration step

pubmed:25784956

Le document en format XML

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<author><name sortKey="Kadi, Adiveppa S" sort="Kadi, Adiveppa S" uniqKey="Kadi A" first="Adiveppa S" last="Kadi">Adiveppa S. Kadi</name>
<affiliation><nlm:affiliation>Department of Studies in Statistics, Karnatak University, Dharwad 580003, India.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Avaradi, Shivakumari R" sort="Avaradi, Shivakumari R" uniqKey="Avaradi S" first="Shivakumari R" last="Avaradi">Shivakumari R. Avaradi</name>
<affiliation><nlm:affiliation>Department of Studies in Statistics, Karnatak University, Dharwad 580003, India.</nlm:affiliation>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">A Bayesian inferential approach to quantify the transmission intensity of disease outbreak.</title>
<author><name sortKey="Kadi, Adiveppa S" sort="Kadi, Adiveppa S" uniqKey="Kadi A" first="Adiveppa S" last="Kadi">Adiveppa S. Kadi</name>
<affiliation><nlm:affiliation>Department of Studies in Statistics, Karnatak University, Dharwad 580003, India.</nlm:affiliation>
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<author><name sortKey="Avaradi, Shivakumari R" sort="Avaradi, Shivakumari R" uniqKey="Avaradi S" first="Shivakumari R" last="Avaradi">Shivakumari R. Avaradi</name>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term>
<term>Basic Reproduction Number</term>
<term>Bayes Theorem</term>
<term>Disease Outbreaks</term>
<term>Humans</term>
<term>India</term>
<term>Influenza A Virus, H1N1 Subtype</term>
<term>Influenza, Human (epidemiology)</term>
<term>Influenza, Human (transmission)</term>
<term>Models, Statistical</term>
<term>Probability</term>
<term>Software</term>
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<keywords scheme="MESH" type="geographic" xml:lang="en"><term>India</term>
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<keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Influenza, Human</term>
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<keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Influenza, Human</term>
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<keywords scheme="MESH" xml:lang="en"><term>Algorithms</term>
<term>Basic Reproduction Number</term>
<term>Bayes Theorem</term>
<term>Disease Outbreaks</term>
<term>Humans</term>
<term>Influenza A Virus, H1N1 Subtype</term>
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<front><div type="abstract" xml:lang="en">Emergence of infectious diseases like influenza pandemic (H1N1) 2009 has become great concern, which posed new challenges to the health authorities worldwide. To control these diseases various studies have been developed in the field of mathematical modelling, which is useful tool for understanding the epidemiological dynamics and their dependence on social mixing patterns.</div>
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<DateCompleted><Year>2015</Year>
<Month>12</Month>
<Day>17</Day>
</DateCompleted>
<DateRevised><Year>2020</Year>
<Month>03</Month>
<Day>06</Day>
</DateRevised>
<Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1748-6718</ISSN>
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<Title>Computational and mathematical methods in medicine</Title>
<ISOAbbreviation>Comput Math Methods Med</ISOAbbreviation>
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<ArticleTitle>A Bayesian inferential approach to quantify the transmission intensity of disease outbreak.</ArticleTitle>
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<Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Emergence of infectious diseases like influenza pandemic (H1N1) 2009 has become great concern, which posed new challenges to the health authorities worldwide. To control these diseases various studies have been developed in the field of mathematical modelling, which is useful tool for understanding the epidemiological dynamics and their dependence on social mixing patterns.</AbstractText>
<AbstractText Label="METHOD" NlmCategory="METHODS">We have used Bayesian approach to quantify the disease outbreak through key epidemiological parameter basic reproduction number (R0), using effective contacts, defined as sum of the product of incidence cases and probability of generation time distribution. We have estimated R0 from daily case incidence data for pandemic influenza A/H1N1 2009 in India, for the initial phase.</AbstractText>
<AbstractText Label="RESULT" NlmCategory="RESULTS">The estimated R0 with 95% credible interval is consistent with several other studies on the same strain. Through sensitivity analysis our study indicates that infectiousness affects the estimate of R0.</AbstractText>
<AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">Basic reproduction number R0 provides the useful information to the public health system to do some effort in controlling the disease by using mitigation strategies like vaccination, quarantine, and so forth.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Kadi</LastName>
<ForeName>Adiveppa S</ForeName>
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<AffiliationInfo><Affiliation>Department of Studies in Statistics, Karnatak University, Dharwad 580003, India.</Affiliation>
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<Author ValidYN="Y"><LastName>Avaradi</LastName>
<ForeName>Shivakumari R</ForeName>
<Initials>SR</Initials>
<AffiliationInfo><Affiliation>Department of Studies in Statistics, Karnatak University, Dharwad 580003, India.</Affiliation>
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<MeshHeading><DescriptorName UI="D001499" MajorTopicYN="N">Bayes Theorem</DescriptorName>
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A Bayesian inferential approach to quantify the transmission intensity of disease outbreak.

A Bayesian inferential approach to quantify the transmission intensity of disease outbreak.

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English descriptors

Abstract

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