SrasV1, PubMed, Curation, bibRecord, 000E37

The impact of prior information on estimates of disease transmissibility using Bayesian tools.

Identifieur interne : 000E37 ( PubMed/Curation ); précédent : 000E36; suivant : 000E38

The impact of prior information on estimates of disease transmissibility using Bayesian tools.

Auteurs : Carlee B. Moser [États-Unis] ; Mayetri Gupta [États-Unis] ; Brett N. Archer [Afrique du Sud] ; Laura F. White [États-Unis]

Source :

PloS one [ 1932-6203 ] ; 2015.

RBID : pubmed:25793993

Descripteurs français

KwdFr :
- Flambées de maladies (), Grippe humaine (transmission), Grippe humaine (virologie), Grippe humaine (épidémiologie), Hong Kong (épidémiologie), Humains, Intervalles de confiance, République d'Afrique du Sud (épidémiologie), Simulation numérique, Singapour (épidémiologie), Sous-type H1N1 du virus de la grippe A, Syndrome respiratoire aigu sévère (épidémiologie), Théorème de Bayes, Traçage des contacts.
MESH :
- virologie : Grippe humaine.
- épidémiologie : Grippe humaine, Hong Kong, République d'Afrique du Sud, Singapour, Syndrome respiratoire aigu sévère.
- Flambées de maladies, Humains, Intervalles de confiance, Simulation numérique, Sous-type H1N1 du virus de la grippe A, Théorème de Bayes, Traçage des contacts.
Wicri :
- geographic : Hong Kong, Singapour, Afrique du Sud.

English descriptors

KwdEn :
- Bayes Theorem, Computer Simulation, Confidence Intervals, Contact Tracing, Disease Outbreaks (statistics & numerical data), Hong Kong (epidemiology), Humans, Influenza A Virus, H1N1 Subtype, Influenza, Human (epidemiology), Influenza, Human (transmission), Influenza, Human (virology), Severe Acute Respiratory Syndrome (epidemiology), Singapore (epidemiology), South Africa (epidemiology).
MESH :
- geographic , epidemiology : Hong Kong, Singapore, South Africa.
- epidemiology : Influenza, Human, Severe Acute Respiratory Syndrome.
- statistics & numerical data : Disease Outbreaks.
- transmission : Influenza, Human.
- virology : Influenza, Human.
- Bayes Theorem, Computer Simulation, Confidence Intervals, Contact Tracing, Humans, Influenza A Virus, H1N1 Subtype.

Abstract

The basic reproductive number (R₀) and the distribution of the serial interval (SI) are often used to quantify transmission during an infectious disease outbreak. In this paper, we present estimates of R₀ and SI from the 2003 SARS outbreak in Hong Kong and Singapore, and the 2009 pandemic influenza A(H1N1) outbreak in South Africa using methods that expand upon an existing Bayesian framework. This expanded framework allows for the incorporation of additional information, such as contact tracing or household data, through prior distributions. The results for the R₀ and the SI from the influenza outbreak in South Africa were similar regardless of the prior information (R0 = 1.36-1.46, μ = 2.0-2.7, μ = mean of the SI). The estimates of R₀ and μ for the SARS outbreak ranged from 2.0-4.4 and 7.4-11.3, respectively, and were shown to vary depending on the use of contact tracing data. The impact of the contact tracing data was likely due to the small number of SARS cases relative to the size of the contact tracing sample.

DOI: 10.1371/journal.pone.0118762
PubMed: 25793993

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Le document en format XML

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<front><div type="abstract" xml:lang="en">The basic reproductive number (R₀) and the distribution of the serial interval (SI) are often used to quantify transmission during an infectious disease outbreak. In this paper, we present estimates of R₀ and SI from the 2003 SARS outbreak in Hong Kong and Singapore, and the 2009 pandemic influenza A(H1N1) outbreak in South Africa using methods that expand upon an existing Bayesian framework. This expanded framework allows for the incorporation of additional information, such as contact tracing or household data, through prior distributions. The results for the R₀ and the SI from the influenza outbreak in South Africa were similar regardless of the prior information (R0 = 1.36-1.46, μ = 2.0-2.7, μ = mean of the SI). The estimates of R₀ and μ for the SARS outbreak ranged from 2.0-4.4 and 7.4-11.3, respectively, and were shown to vary depending on the use of contact tracing data. The impact of the contact tracing data was likely due to the small number of SARS cases relative to the size of the contact tracing sample.</div>
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{{Explor lien
   |wiki=    Sante
   |area=    SrasV1
   |flux=    PubMed
   |étape=   Curation
   |type=    RBID
   |clé=     pubmed:25793993
   |texte=   The impact of prior information on estimates of disease transmissibility using Bayesian tools.
}}

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       | NlmPubMed2Wicri -a SrasV1

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The impact of prior information on estimates of disease transmissibility using Bayesian tools.

The impact of prior information on estimates of disease transmissibility using Bayesian tools.

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