Parameter estimation of respiratory infection surveillance dynamics commonly utilize data aggregated over space and time. However, estimates derived from aggregated data may fail to account for biologically meaningful spatiotemporal heterogeneity of effects or to identify where and when transmissions occur. This dissertation shows that high-resolution temporal and spatial data can improve our understanding of heterogeneity while producing more valid and precise estimates of transmission parameters (e.g., contagiousness), behavioral trends (e.g., face mask utilization), and intervention effects (e.g., at-home test distribution). In three projects, we evaluate spatiotemporal heterogeneity in the context of two major respiratory pathogens: Tuberculosis and SARSCoV-2. First, in project one, we identify disease transmission hotspots from a tuberculosis case surveillance system in Greater Vitória, Brazil. Utilizing a human mobility model and recently developed method to quantify disease transmission, we overcome multiple methodological constraints that often obscure spatially and temporally accurate transmission measurements. We estimate that two cities in Greater Vitória, Vila Velha (reproductive number = 1.05, 95%CI: 1.03–1.07) and Vitória (reproductive number = 1.04, 95%CI: 1.02–1.06), help sustain tuberculosis transmission in the entire region and may be effective targets for intervention, while Cariacica (reproductive number = 0.95, 95%CI: 0.94–0.97) fell below the critical threshold of 1 required to sustain transmission alone. Next, in project two, we utilize interrupted time series methods to estimate the effect of mask mandates on mask adherence using a nationally representative digital health survey on masking and a comprehensive database of pandemic-related government policies. The analysis focuses on improving previous attempts at measuring the effectiveness of mask mandates at the state level, by utilizing county-level exposure and outcome data. We find that mask mandates were associated with a large heterogeneity of effects, ranging from increasing masking approximately 8% in counties with low levels of prior masking to 1% or lower change in masking in places like the Northeast U.S. where masking levels were already high. Last, in project three, we leverage the same nationally representative digital health survey to understand at-home testing patterns in the United States. We utilize two different economic measures of resource allocation and a regression model with autoregressive integrated moving average errors to examine if the Covidtests.gov government program reduced at-home testing inequities. We show that Covidtest.gov did increase at-home testing across all demographics; however, income-, geographic- and race-based disparities in at-home test utilization were heightened during periods when the program was active. Specifically, the regression results estimate that Theil’s T, an economic metric used here to measure at-home testing disparities, was 53% (95%CI: 6%–121%) higher for household income, 214% (95%CI: 86%–429%) higher for race, and 90% (95%CI: 23%–193%) higher for geography during Covidtest.gov dissemination periods. Disparities were not elevated for age. Together, these three projects demonstrate the substantial role that high-resolution data can play in improving our understanding of respiratory infection surveillance and informing effective public health interventions.