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Activity Number:
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146
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Type:
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Contributed
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Date/Time:
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Monday, August 7, 2006 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #307411 |
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Title:
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Prospective Surveillance of Influenza Data Using Hidden Markov Models
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Author(s):
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Al Ozonoff*+ and Paola Sebastiani
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Companies:
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Boston University and Boston University
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Address:
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715 Albany Street, T4E, School of Public Health, Boston, MA, 02118,
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Keywords:
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influenza ; surveillance ; hidden Markov models
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Abstract:
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We propose a Hidden Markov model approach to investigating spatio-temporal patterns in influenza mortality data. Motivated by prospective surveillance methods, as used in syndromic surveillance, we consider various levels of spatial aggregation and calculate deviations from expected patterns of disease in order to better understand the dynamics in both space and time of influenza transmission, morbidity, and mortality. We demonstrate the improved fit of HMMs to influenza data and consider the implications for prospective surveillance efforts at the national, regional, and local levels.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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