  Much traditional statistical modelling assumes that the outcome variables of
interest are independent of each other when conditioned on the explanatory
variables. This assumption is strongly violated in the case of infectious
diseases, particularly in close-contact settings such as households, where each
individual's probability of infection is strongly influenced by whether other
household members experience infection. On the other hand, general multi-type
transmission models of household epidemics quickly become unidentifiable from
data as the number of types increases. This has led to a situation where it is
has not been possible to draw consistent conclusions from household studies of
infectious diseases, for example in the event of an influenza pandemic. Here,
we present a generalised linear modelling framework for binary outcomes in
sub-units that can (i) capture the effects of non-independence arising from a
transmission process and (ii) adjust estimates of disease risk and severity for
differences in study population characteristics. This model allows for
computationally fast estimation, uncertainty quantification, covariate choice
and model selection. In application to real pandemic influenza household data,
we show that it is formally favoured over existing modelling approaches.
