Simple jQuery Dropdowns
Please use this identifier to cite or link to this item:
Title: Bayesian modeling to unmask and predict influenza A/H1N1pdm dynamics in London
Authors: Paul J. Birrell
Georgios Ketsetzis
Nigel J. Gay
Ben S. Cooper
Anne M. Presanis
Ross J. Harris
André Charlett
Xu Sheng Zhang
Peter J. White
Richard G. Pebody
Daniela De Angelis
University of Cambridge
Health Protection Agency
Fu Consulting
Mahidol University
Imperial College Faculty of Medicine
Keywords: Multidisciplinary
Issue Date: 8-Nov-2011
Citation: Proceedings of the National Academy of Sciences of the United States of America. Vol.108, No.45 (2011), 18238-18243
Abstract: The tracking and projection of emerging epidemics is hindered by the disconnect between apparent epidemic dynamics, discernible from noisy and incomplete surveillance data, and the underlying, imperfectly observed, system. Behavior changes compound this, altering both true dynamics and reporting patterns, particularly for diseases with nonspecific symptoms, such as influenza. We disentangle these effects to unravel the hidden dynamics of the 2009 influenza A/H1N1pdm pandemic in London, where surveillance suggests an unusual dominant peak in the summer. We embed an age-structured model into a Bayesian synthesis of multiple evidence sources to reveal substantial changes in contact patterns and health-seeking behavior throughout the epidemic, uncovering two similar infection waves, despite large differences in the reported levels of disease. We show how this approach, which allows for real-time learning about model parameters as the epidemic progresses, is also able to provide a sequence of nested projections that are capable of accurately reflecting the epidemic evolution.
ISSN: 10916490
Appears in Collections:Scopus 2011-2015

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.