Emerging viruses such as COVID-19 and dengue pose substantial public health risks in large cities, but their impact on host populations can be quite heterogeneous and hard to quantify using traditional mathematical models. I quantify the extent and impact of heterogeneity in infection status and in underlying transmission for both diseases. In my first chapter, I use a model that incorporates daily changes in testing capacity to precisely quantify the proportion of COVID-19 cases in New York City that were symptomatic during the initial epidemic wave. In my second chapter, I demonstrate that susceptible depletion on a city- wide aggregate level cannot explain the rapid re-emergence of dengue serotype DENV1 in the 1980s in Rio de Janeiro, Brazil, and suggest that inter-annual variation in climate, spatial heterogeneity within a large city, and coupling between cities may play an important role in dengue dynamics. In my third chapter, I use a panel of mechanistic models driven by cases from Rio to show that connectivity to the city of Rio played a crucial role in the spread of DENV4 through the Rio metropolitan area in 2012-2013, and that secondary movement hubs in the suburbs east of Rio could also be very important in facilitating the virus’ spread to more outlying areas. These essential fluxes could be incorporated into statistical models that can already capture inter-annual variation in climate and socio-economic variables or integrated with smaller-scale mechanistic models to provide a multi-scale understanding of dengue transmission and re-emergence.