Bayesian modeling to unmask and predict influenza A/H1N1pdm dynamics in London. Birrell, P. J., Ketsetzis, G., Gay, N. J., Cooper, B. S., Presanis, A. M., Harris, R. J., Charlett, A., Zhang, X., White, P. J., Pebody, R. G., & De Angelis, D. Proceedings of the National Academy of Sciences of the United States of America, 108(45):18238--18243, November, 2011. doi abstract bibtex 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.
@article{birrell_bayesian_2011,
title = {Bayesian modeling to unmask and predict influenza {A}/{H}1N1pdm dynamics in {London}},
volume = {108},
issn = {1091-6490},
doi = {10.1073/pnas.1103002108},
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.},
language = {eng},
number = {45},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
author = {Birrell, Paul J. and Ketsetzis, Georgios and Gay, Nigel J. and Cooper, Ben S. and Presanis, Anne M. and Harris, Ross J. and Charlett, André and Zhang, Xu-Sheng and White, Peter J. and Pebody, Richard G. and De Angelis, Daniela},
month = nov,
year = {2011},
pmid = {22042838},
pmcid = {PMC3215054},
keywords = {Bayes Theorem, Humans, Influenza A Virus, H1N1 Subtype, Influenza, Human, London},
pages = {18238--18243}
}
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