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Journal article

Bayesian modeling to unmask and predict influenza A/H1N1pdm dynamics in London.

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 ...

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Publication status:
Published

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Publisher copy:
10.1073/pnas.1103002108

Authors


Birrell, PJ More by this author
Ketsetzis, G More by this author
More by this author
Institution:
University of Oxford
Department:
Oxford, MSD, Clinical Medicine, Thailand/Laos MOP
Presanis, AM More by this author
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Journal:
Proceedings of the National Academy of Sciences of the United States of America
Volume:
108
Issue:
45
Pages:
18238-18243
Publication date:
2011-11-05
DOI:
EISSN:
1091-6490
ISSN:
0027-8424
URN:
uuid:433f4a41-d197-4909-ac5e-9fa2b54c6007
Source identifiers:
206173
Local pid:
pubs:206173

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