Mathematical models of infectious disease are increasingly capable of capturing spatial and demographic factors in transmission. However, there has been limited evaluation of how ethnic and socioeconomic groups within a population might impact transmission, the effectiveness of interventions and inequalities in infectious disease outcomes. A large part of this challenge lies in identifying means by which information about how social groups interact can be measured and included in mechanistic models of transmission. By means of data analysis and mathematical modelling, I have investigated how social groups contribute to heterogeneity in transmission and how these factors may be captured in a model of transmission. In the first part of this thesis I first present my evaluation of the roles of transmission and vaccination differences between social groups in creating inequalities in disease risk. Secondly, I report my analysis of reported cases from the 2009 Influenza H1N1 outbreak to elucidate the spatial and social nature of the early stages of the outbreak. Later I present a novel framework that I have developed for analysis of social contact of school aged children and modelling transmission. This framework utilises national school and pupil data to simulate outbreaks over a network, explicitly accounting for school and household transmission links. Finally, I present the application of this framework in two distinct settings: First, I assess the potential role of the school system in inequalities in influenza risk between ethnic and socioeconomic groups in London. Then I investigate how connections between schools and households in the Netherlands might impact clustering of children unvaccinated against measles. Finally, I evaluate how such clustering impacts the epidemiology of measles in The Netherlands, where vaccine refusal is clearly associated to particular socio-religious communities. I find evidence that inequalities in disease are most sensitive to differences in transmission if the pathogen has a low basic reproduction number. With higher basic reproduction numbers, inequalities are more sensitive to variation in vaccine uptake. Inequalities observed in influenza are not clearly reconciled by the school network structure, however the network may promote inequalities in incidence early in an outbreak, which may be interpreted as inequality in risk. Finally, school networks can explain the observed measles dynamics in the Netherlands well, reproducing the outbreak scale and geographical spread of cases reported in recent outbreaks.