SrasV1, Main, Exploration, bibRecordById, pubmed:24043437

A new framework and software to estimate time-varying reproduction numbers during epidemics.

Identifieur interne : 001A03 ( Main/Exploration ); précédent : 001A02; suivant : 001A04

A new framework and software to estimate time-varying reproduction numbers during epidemics.

Auteurs : Anne Cori ; Neil M. Ferguson ; Christophe Fraser ; Simon Cauchemez

Source :

American journal of epidemiology [ 1476-6256 ] ; 2013.

RBID : pubmed:24043437

Descripteurs français

KwdFr :
- Facteurs temps, Humains, Incidence, Logiciel, Maladies virales (transmission), Maladies virales (épidémiologie), Modèles statistiques, Épidémies ().
MESH :
- épidémiologie : Maladies virales.
- Facteurs temps, Humains, Incidence, Logiciel, Modèles statistiques, Épidémies.

English descriptors

KwdEn :
- Epidemics (statistics & numerical data), Humans, Incidence, Models, Statistical, Software, Time Factors, Virus Diseases (epidemiology), Virus Diseases (transmission).
MESH :
- epidemiology : Virus Diseases.
- statistics & numerical data : Epidemics.
- transmission : Virus Diseases.
- Humans, Incidence, Models, Statistical, Software, Time Factors.

Abstract

The quantification of transmissibility during epidemics is essential to designing and adjusting public health responses. Transmissibility can be measured by the reproduction number R, the average number of secondary cases caused by an infected individual. Several methods have been proposed to estimate R over the course of an epidemic; however, they are usually difficult to implement for people without a strong background in statistical modeling. Here, we present a ready-to-use tool for estimating R from incidence time series, which is implemented in popular software including Microsoft Excel (Microsoft Corporation, Redmond, Washington). This tool produces novel, statistically robust analytical estimates of R and incorporates uncertainty in the distribution of the serial interval (the time between the onset of symptoms in a primary case and the onset of symptoms in secondary cases). We applied the method to 5 historical outbreaks; the resulting estimates of R are consistent with those presented in the literature. This tool should help epidemiologists quantify temporal changes in the transmission intensity of future epidemics by using surveillance data.

DOI: 10.1093/aje/kwt133
PubMed: 24043437

Affiliations:

Le document en format XML

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<keywords scheme="MESH" xml:lang="fr"><term>Facteurs temps</term>
<term>Humains</term>
<term>Incidence</term>
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<front><div type="abstract" xml:lang="en">The quantification of transmissibility during epidemics is essential to designing and adjusting public health responses. Transmissibility can be measured by the reproduction number R, the average number of secondary cases caused by an infected individual. Several methods have been proposed to estimate R over the course of an epidemic; however, they are usually difficult to implement for people without a strong background in statistical modeling. Here, we present a ready-to-use tool for estimating R from incidence time series, which is implemented in popular software including Microsoft Excel (Microsoft Corporation, Redmond, Washington). This tool produces novel, statistically robust analytical estimates of R and incorporates uncertainty in the distribution of the serial interval (the time between the onset of symptoms in a primary case and the onset of symptoms in secondary cases). We applied the method to 5 historical outbreaks; the resulting estimates of R are consistent with those presented in the literature. This tool should help epidemiologists quantify temporal changes in the transmission intensity of future epidemics by using surveillance data.</div>
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<name sortKey="Fraser, Christophe" sort="Fraser, Christophe" uniqKey="Fraser C" first="Christophe" last="Fraser">Christophe Fraser</name>
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A new framework and software to estimate time-varying reproduction numbers during epidemics.

A new framework and software to estimate time-varying reproduction numbers during epidemics.

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

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