  Understanding age-group dynamics of infectious diseases is a fundamental
issue for both scientific study and policymaking. Age-structure epidemic models
were developed in order to study and improve our understanding of these
dynamics. By fitting the models to incidence data of real outbreaks one can
infer estimates of key epidemiological parameters. However, estimation of the
transmission in an age-structured populations requires first to define the
age-groups of interest. Misspecification in representing the heterogeneity in
the age-dependent transmission rates can potentially lead to biased estimation
of parameters. We develop the first statistical, data-driven methodology for
deciding on the best partition of incidence data into age-groups. The method
employs a top-down hierarchical partitioning algorithm, with a metric distance
built for maximizing mathematical identifiability of the transmission matrix,
and a stopping criteria based on significance testing. The methodology is
tested using simulations showing good statistical properties. The methodology
is then applied to influenza incidence data of 14 seasons in order to extract
the significant age-group clusters in each season.
