Joint modelling of serological and hospitalization data reveals that high levels of pre-existing immunity and school holidays shaped the influenza A pandemic of 2009 in the Netherlands. ...
Obtaining a quantitative understanding of the transmission dynamics of influenza A is important for predicting healthcare demand and assessing the likely impact of intervention measures. The pandemic of 2009 provides an ideal platform for developing integrative analyses as it has been studied intensively, and a wealth of data sources is available. Here, we analyse two complementary datasets in a disease transmission framework: cross-sectional serological surveys providing data on infection attack rates, and hospitalization data that convey information on the timing and duration of the pandemic... Mehr ...
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Dokumenttyp: | Scholarlyarticle |
Erscheinungsdatum: | 2015 |
Verlag/Hrsg.: |
The Royal Society
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Schlagwörter: | Bayesian evidence synthesis / hospitalization incidence / influenza A / mixture analysis / serology / transmission model / Adolescent / Adult / Age Factors / Aged / Child / Preschool / Cross-Sectional Studies / Female / Hospitalization / Humans / Influenza / Human / Male / Middle Aged / Models / Biological / Netherlands / Pandemics |
Sprache: | Englisch |
Permalink: | https://search.fid-benelux.de/Record/base-29160437 |
Datenquelle: | BASE; Originalkatalog |
Powered By: | BASE |
Link(s) : | https://dx.doi.org/10.17863/cam.25516 |
Obtaining a quantitative understanding of the transmission dynamics of influenza A is important for predicting healthcare demand and assessing the likely impact of intervention measures. The pandemic of 2009 provides an ideal platform for developing integrative analyses as it has been studied intensively, and a wealth of data sources is available. Here, we analyse two complementary datasets in a disease transmission framework: cross-sectional serological surveys providing data on infection attack rates, and hospitalization data that convey information on the timing and duration of the pandemic. We estimate key epidemic determinants such as infection and hospitalization rates, and the impact of a school holiday. In contrast to previous approaches, our novel modelling of serological data with mixture distributions provides a probabilistic classification of individual samples (susceptible, immune and infected), propagating classification uncertainties to the transmission model and enabling serological ...