Literature DB >> 23337238

Estimating the generation interval of influenza A (H1N1) in a range of social settings.

Dennis E te Beest1, Jacco Wallinga, Tjibbe Donker, Michiel van Boven.   

Abstract

A proper understanding of the infection dynamics of influenza A viruses hinges on the availability of reliable estimates of key epidemiologic parameters such as the reproduction number, intrinsic growth rate, and generation interval. Often the generation interval is assumed to be similar in different settings although there is little evidence justifying this. Here we estimate the generation interval for stratifications based on age, cluster size, and social setting (camp, school, workplace, household) using data from 16 clusters of Novel Influenza A (H1N1) in the Netherlands. Our analyses are based on a Bayesian inferential framework, enabling flexible handling of both missing infection links and missing times of symptoms onset. The analysis indicates that a stratification that allows the generation interval to differ by social setting fits the data best. Specifically, the estimated generation interval was shorter in households (2.1 days [95% credible interval = 1.6-2.9]) and camps (2.3 days [1.4-3.4]) than in workplaces (2.7 days [1.9-3.7]) and schools (3.4 days [2.5-4.5]). Our findings could be the result of differences in the number of contacts between settings, differences in prophylactic use of antivirals between settings, and differences in underreporting.

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Year:  2013        PMID: 23337238     DOI: 10.1097/EDE.0b013e31827f50e8

Source DB:  PubMed          Journal:  Epidemiology        ISSN: 1044-3983            Impact factor:   4.822


  7 in total

1.  Cost-effective length and timing of school closure during an influenza pandemic depend on the severity.

Authors:  Hiroshi Nishiura; Keisuke Ejima; Kenji Mizumoto; Shinji Nakaoka; Hisashi Inaba; Seiya Imoto; Rui Yamaguchi; Masaya M Saito
Journal:  Theor Biol Med Model       Date:  2014-01-21       Impact factor: 2.432

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

Authors:  Carlee B Moser; Mayetri Gupta; Brett N Archer; Laura F White
Journal:  PLoS One       Date:  2015-03-20       Impact factor: 3.240

3.  Forward-looking serial intervals correctly link epidemic growth to reproduction numbers.

Authors:  Sang Woo Park; Kaiyuan Sun; David Champredon; Michael Li; Benjamin M Bolker; David J D Earn; Joshua S Weitz; Bryan T Grenfell; Jonathan Dushoff
Journal:  Proc Natl Acad Sci U S A       Date:  2021-01-12       Impact factor: 11.205

4.  Nine-month Trend of Time-Varying Reproduction Numbers of COVID-19 in West of Iran.

Authors:  Ebrahim Rahimi; Seyed Saeed Hashemi Nazari; Yaser Mokhayeri; Asaad Sharhani; Rasool Mohammadi
Journal:  J Res Health Sci       Date:  2021-06-28

5.  How to interpret the transmissibility of novel influenza A(H7N9): an analysis of initial epidemiological data of human cases from China.

Authors:  Hiroshi Nishiura; Kenji Mizumoto; Keisuke Ejima
Journal:  Theor Biol Med Model       Date:  2013-05-04       Impact factor: 2.432

6.  Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020.

Authors:  Tapiwa Ganyani; Cécile Kremer; Dongxuan Chen; Andrea Torneri; Christel Faes; Jacco Wallinga; Niel Hens
Journal:  Euro Surveill       Date:  2020-04

7.  Estimates of serial interval for COVID-19: A systematic review and meta-analysis.

Authors:  Balram Rai; Anandi Shukla; Laxmi Kant Dwivedi
Journal:  Clin Epidemiol Glob Health       Date:  2020-08-26
  7 in total

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