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Sexually transmitted infections impose a huge burden on public health. The epidemic trajectories of such infections form part of a complex system of pathogen transmission, and are dependent on both pathogen characteristics and the structure and dynamics of the underlying sexual contact network. This thesis uses a dynamic network model which realistically represents human sexual contact dynamics in order to investigate the interactions be- tween these factors via three different approaches. Firstly, a transmission model is used to evaluate the role of pathogen du- ration of infectiousness in shaping the effective network over which an STI may spread. This allows us to examine the way in which the importance of different individual-level risk factors (such as number of sexual partners over five years) differs based on pathogen properties. Next, we expand this model to include evolution of a pathogen's genetic sequence, in order to study the extent to which population structure affects the structure of phylogenetic trees resulting from sampled sequences. Finally, we use this same transmission and evolution model to look at the skyline method of reconstructing past changes in effective population size, in order to estimate the number of sequence samples needed to be able to distinguish between a stable and a growing pathogen population and to ~ investigate the effect of network dynamics on the accuracy of this method.