Outbreaks of infectious disease pose an enormous global burden on human well-being. Effectively controlling such outbreaks requires timely and evidence-based interventions, which are increasingly being informed by statistical modelling efforts. A specific field of statistical modelling attempts to reconstruct the history of transmission events between individual cases in order to provide high-resolution epidemiological insights directly relevant to infection control. Recent years have seen considerable methodological progress in this regard, with an emphasis on using whole genome sequencing (WGS) data. However, current methods largely disregard other types of outbreak data and are therefore poorly suited to outbreaks where WGS data is unavailable or weakly informative. This thesis aims to improve the utility and widen the scope of outbreak reconstruction tools by incorporating additional types of outbreak data. I begin with an overview of this field of research (Chapter 1). I then conduct a simulation study to evaluate the realistic informativeness of WGS data for different pathogens (Chapter 2). Next, I develop methods and software for integrating commonly available epidemiological data, namely contact data (Chapter 3) and hospital ward data (Chapter 4), into the inference of transmission chains. Finally, I consider outbreak reconstruction in a more operational context. I develop a simple algorithm and software tool for visualising timed transmission networks (Chapter 5) and then apply the methods developed in this thesis to synthesized data resembling the ongoing Ebola outbreak in the Democratic Republic of Congo (Chapter 6). In the last chapter, I discuss the methodological components of this thesis and end on a more general reflection of how outbreak reconstruction tools can be realistically used in outbreak settings to reduce the burden of disease.