Modeling the initial transmission dynamics of influenza A H1N1 in Guangdong Province, China

https://doi.org/10.1016/j.ijid.2012.11.018Get rights and content
Under an Elsevier user license
open archive

Summary

Background

The novel influenza A H1N1 (2009) virus, identified in mid-2009, spread rapidly in Guangdong Province. The accurate estimation of epidemiological parameters is of vital significance in decision-making for coping with pandemic influenza.

Methods

We used influenza A H1N1 epidemic data from local cases in Guangdong Province, China, in conjunction with a complex SEIR model (susceptible, exposed, infectious, recovered) to estimate the basic reproduction number. The transmission rate was obtained by fitting the model to the cumulative number of local daily infected cases using the nonlinear ordinary least squares method. The latent period and duration of infectiousness were obtained from the published literature, and the proportion of symptomatic infected cases was obtained from the serological survey conducted by the Center for Disease Control and Prevention of Guangdong Province. We determined the variance of model parameters via a simulation study.

Results

The model was in keeping with the observed epidemic data (coefficient of determination = 0.982). The basic reproduction number was estimated preliminarily to be R0 = 1.525 (95% confidence interval 1.448–1.602), with the possible range of true R0 being 1.30–1.85. We estimated the transmission rate β to be between 0.390 and 0.432.

Conclusions

With the help of the serological survey, useful estimates of key epidemiological parameters for the influenza A H1N1 outbreak in Guangdong Province were obtained. The sensitivity analysis suggests that different latent periods and infectious periods, which specify different mean durations of generation time, have a significant impact on R0. Our proposed model and findings provide a relevant contribution towards understanding the characteristics of influenza A H1N1 in Guangdong Province.

Keywords

Influenza A
Modeling
Pandemic
Public health

Cited by (0)