The aim of this theme is to develop a methodological framework in which genomic data can be integrated with other types of complex data, including contact network and behavioural data. Such epidemiological data is routinely gathered by UKHSA (e.g. from case based surveillance systems, population-based surveys, contact tracing initiatives) and subject to expert review, but a framework for co-analysing with genomic relatedness data is lacking. To address this, our strategy is to use well established models of infectious disease epidemiology, which can account for the complex transmission dynamics of infectious diseases in subpopulations. These will be extended to accommodate additional data streams including genomic data. This framework will incorporate all available data sources to construct infectious disease models with which we can test hypotheses (e.g. the relative importance of different transmission routes) and estimate important epidemiological parameters, such as the distribution of the generation time from one transmission to the next, properties of underlying contact networks, or the level of infectiousness in different phases of a disease. These parameter estimates will feed into other Themes. This is a powerful approach independent of those described in Theme 1 through the fact that they are not specific to outbreaks, and start with a complex model to interpret genomic data in this context, rather than starting with the genomic data.