Other limitations relate to the simplicity of the model structure. We assumed homogenous mixing and a constant basic reproduction number across all populations. In reality, heterogeneities in factors such as age-structure, geographic structure, population density, human behavior, and the underlying health of the population are all likely to play a role in transmission dynamics and burden of influenza. A previous modeling analysis, for example, has shown that higher levels of population heterogeneities, such as in age and spatial structuring of contacts, result in lower overall attack rates and peak prevalence for a given basic reproduction number [33] . However, there is a lack of data on such heterogeneities and how they might affect patterns of pandemic progression for our study region. Ongoing studies, such as contact pattern surveys in Asia similar to those undertaken in Europe [34] , are attempting to rectify this. Another limitation is that the resource data were collected between May and September 2009, which includes the first wave of the H1N1-2009 pandemic; thus some resource data (particularly for antiviral stockpiles) may be influenced by the time point within this period at which the data were recorded.