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)
Year published
2013
Metadata
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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 ...
View more >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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View more >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
Subject
Biomedical and clinical sciences