Emerging and re-emerging infections such as SARS (2003) and pandemic H1N1 (2009)
have caused concern for public health researchers and policy makers due to the increased
burden of these diseases on health care systems. This concern has prompted the use of
mathematical models to evaluate strategies to control disease spread, making these models
invaluable tools to identify optimal intervention strategies. A particularly important quantity
in infectious disease epidemiology is the basic reproduction number, R0. Estimation of
this quantity is crucial for effective control responses in the early phase of an epidemic. In
our previous study, an approach for estimating the basic reproduction number in real time
was developed. This approach uses case notification data and the structure of potential
transmission contacts to accurately estimate R0 from the limited amount of information
available at the early stage of an outbreak. Based on this approach, we extend the existing
methodology; the most recent method features intra- and inter-age groups contact heterogeneity.
Given the number of newly reported cases at the early stage of the outbreak, with
parsimony assumptions on removal distribution and infectivity profile of the diseases, experiments
to estimate real time R0 under different levels of intra- and inter-group contact heterogeneity
using two age groups are presented. We show that the new method converges
more quickly to the actual value of R0 than the previous one, in particular when there is
high-level intra-group and inter-group contact heterogeneity. With the age specific contact
patterns, number of newly reported cases, removal distribution, and information about the natural history of the 2009 pandemic influenza in Hong Kong, we also use the extended
model to estimate R0 and age-specific R0.