Since 2000, we have been trying to characterize and classify HIV epidemics to guide the strategic design of HIV prevention policies and focus HIV programmes and resource allocation by a regions’ epidemic type. We have used arbitrary thresholds of HIV prevalence across different risk-groups in a given population, ‘static’ mathematical models and classical epidemiological measures of the population attributable fraction that do not account for chains of transmission. As a result, these traditional approaches could be missing the underlying transmission dynamics and the role of key populations – such as female sex workers and their clients – on HIV spread. In this thesis, I build on a growing paradigm shift on how we should re-classify HIV epidemics based on the epidemiological features that lead to HIV emergence and persistence (i.e. the ‘epidemic drivers’ that influence the basic reproductive ratio, R0). I examine the extent to which our traditional approaches have been underestimating the contribution of sex work to HIV spread and likely misclassifying epidemic type by developing dynamic mathematical models of HIV transmission and simulating a large number of plausible ‘synthetic’ HIV epidemics. I then develop – as proofof-concept- a novel algorithm to diagnose epidemic type using these synthetic epidemics and glean the key epidemiological data that would be most useful to help distinguish between ‘epidemic drivers’, and therefore would be most useful to collect as part of HIV surveillance and future empirical research.