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

Generalised Linear Models for Dependent Binary Outcomes with Applications to Household Stratified Pandemic Influenza Data