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  • Bayesian estimation of the effective reproduction number for pandemic influenza A H1N1 in Guangdong Province, China

    Author(s)
    Yang, Fen
    Yuan, Lingling
    Tan, Xuhui
    Huang, Cunrui
    Feng, Jun
    Griffith University Author(s)
    Yang, Fuwen
    Year published
    2013
    Metadata
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    Abstract
    Purpose During the course of a pandemic, it is necessary to understand its transmissibility, which is often summarized by the effective reproduction number. Accurate estimation of the effective reproduction number (R) is of vital significance in real-time decision making for coping with pandemic influenza. Methods We used daily case notification data in Guangdong Province, China, in conjunction with Bayesian inference of two different stochastic susceptible, infectious, recovered (SIR) models to estimate the effective reproduction number. The duration of infectiousness was taken from published literature, and the proportion ...
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    Purpose During the course of a pandemic, it is necessary to understand its transmissibility, which is often summarized by the effective reproduction number. Accurate estimation of the effective reproduction number (R) is of vital significance in real-time decision making for coping with pandemic influenza. Methods We used daily case notification data in Guangdong Province, China, in conjunction with Bayesian inference of two different stochastic susceptible, infectious, recovered (SIR) models to estimate the effective reproduction number. The duration of infectiousness was taken from published literature, and the proportion of imported cases was obtained from individual-level data. Results At the initial epidemic phase, 40% of the first 261 cases were not locally acquired. Explicitly accounting for imported cases and different infectious periods, the possible range of basic reproduction number was preliminarily estimated to be between 1.05 and 1.46. We showed how the daily case reports provided valuable information to estimate the effective reproduction number. We also found the potential delay in reporting had a relatively minor impact on estimating R. Conclusions Our proposed models and findings provide a relevant contribution towards establishing a basis for monitoring the evolution of emerging infectious diseases in real time and understanding the characteristics of pandemic influenza A H1N1 in Guangdong Province.
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    Journal Title
    Annals of Epidemiology
    Volume
    23
    Issue
    6
    DOI
    https://doi.org/10.1016/j.annepidem.2013.04.005
    Subject
    Biomedical and clinical sciences
    Publication URI
    http://hdl.handle.net/10072/60205
    Collection
    • Journal articles

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