Bayesian modeling to unmask and predict influenza A/H1N1pdm dynamics in London.
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 pat...
| প্রধান লেখক: | , , , , , , , , , , |
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| বিন্যাস: | Journal article |
| ভাষা: | English |
| প্রকাশিত: |
2011
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| _version_ | 1826269527199973376 |
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| author | Birrell, P Ketsetzis, G Gay, N Cooper, B Presanis, A Harris, R Charlett, A Zhang, X White, P Pebody, R De Angelis, D |
| author_facet | Birrell, P Ketsetzis, G Gay, N Cooper, B Presanis, A Harris, R Charlett, A Zhang, X White, P Pebody, R De Angelis, D |
| author_sort | Birrell, P |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-06T21:26:27Z |
| format | Journal article |
| id | oxford-uuid:433f4a41-d197-4909-ac5e-9fa2b54c6007 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T21:26:27Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | oxford-uuid:433f4a41-d197-4909-ac5e-9fa2b54c60072022-03-26T14:54:18ZBayesian modeling to unmask and predict influenza A/H1N1pdm dynamics in London.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:433f4a41-d197-4909-ac5e-9fa2b54c6007EnglishSymplectic Elements at Oxford2011Birrell, PKetsetzis, GGay, NCooper, BPresanis, AHarris, RCharlett, AZhang, XWhite, PPebody, RDe Angelis, DThe 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. |
| spellingShingle | Birrell, P Ketsetzis, G Gay, N Cooper, B Presanis, A Harris, R Charlett, A Zhang, X White, P Pebody, R De Angelis, D Bayesian modeling to unmask and predict influenza A/H1N1pdm dynamics in London. |
| title | Bayesian modeling to unmask and predict influenza A/H1N1pdm dynamics in London. |
| title_full | Bayesian modeling to unmask and predict influenza A/H1N1pdm dynamics in London. |
| title_fullStr | Bayesian modeling to unmask and predict influenza A/H1N1pdm dynamics in London. |
| title_full_unstemmed | Bayesian modeling to unmask and predict influenza A/H1N1pdm dynamics in London. |
| title_short | Bayesian modeling to unmask and predict influenza A/H1N1pdm dynamics in London. |
| title_sort | bayesian modeling to unmask and predict influenza a h1n1pdm dynamics in london |
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