Our current pandemic has illustrated how infectious diseases, particularly respiratory transmitted infections, can have devastating effects on human life. These pathogens typically require close contact between a susceptible and infected host for successful transmission. As a result, the nature of contacts among populations of hosts is of extreme importance to the spread of disease. Traditional models are based on a compartmentalization of hosts according to their disease status (i.e., susceptible, infected, recovered) and make strong assumptions about homogenous mixing within the population. This assumption can be inappropriate for populations which have heterogeneous contact patterns. Heterogeneous contact patterns can be modeled using contact networks (graphs where nodes represent individuals and edges represent contacts which can facilitate disease spread). In recent years, many interesting hypotheses about contact networks and disease spread have been put forward in network and public health literature. In this dissertation, I use empirical data analyses and simulations to investigate these hypotheses in the context of two globally important pathogens, tuberculosis and COVID-19. In chapter 2, I investigate whether network centrality and mixing patterns are correlated with male-bias in tuberculosis in a large, social network from Kampala, Uganda. Next, in chapter 3, I focus specifically on the effects of preferential social mixing by sex and whether it can facilitate higher infection rates among males, as recently proposed in public health literature. In chapter 4, I investigate the effects of core-periphery contact networks on disease spread and discuss implications for populations with this contact structure. Lastly, in chapter 5, I model counter-factual scenarios of the spread of COVID-19 inside care, correctional, and meat-packing facilities without interventions and compare the distribution of outbreak sizes to the actual distributions and argue that interventions these facilities have taken have significantly mitigated spread. The work in this thesis is unique because it tests theories derived from analytical methods or proposed in the public health literature using simulations and data analyses. This work extends our understanding of how human contact patterns alter disease spread, provides general insights into infectious disease ecology, and has practical public health recommendations.