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.

Στόχος της παρούσας διατριβής είναι η μελέτη των οδικών ατυχημάτων και ο προσδιορισμός των επικίνδυνων θέσεων (μελανών σημείων) στο οδικό δίκτυο της Περιφέρειας Δυτικής Ελλάδας. Πιο συγκεκριμένα, πραγματοποιήθηκε καταγραφή όλων των τροχαίων ατυχημάτων στους Νομούς Αχαΐα, Αιτωλοακαρνανίας και Ηλείας για τη χρονική περίοδο 2000 - 2012. Η συλλογή στοιχείων έγινε από τα κατά τόπους Τμήματα Τροχαίας των Αστυνομικών διευθύνσεων κάθε νομού, για ολόκληρο το οδικό δίκτυο, δηλαδή εθνικό, επαρχιακό και αστικό. Έγινε καταγραφή του κάθε τροχαίου ατυχήματος, του αριθμού νεκρών και τραυματιών, της ημερομηνίας και της ώρας του συμβάντος, της ακριβούς τοποθεσίας, όπως επίσης και των συνθηκών οδοστρώματος και των περιβαλλοντικών συνθηκών που επικρατούσαν τη στιγμή του συμβάντος. Τα στοιχεία που συλλέχθηκαν ομαδοποιήθηκαν κατάλληλα και επεξεργάστηκαν στατιστικά. Στη συνέχεια για κάποια τμήματα των εθνικών οδών της ΠΔΕ έγινε προσδιορισμός των μελανών σημείων με διαφορετικές αριθμητικές και στατιστικές μεθόδους. Δεν έγινε μελέτη των μελανών σημείων για όλο το οδικό δίκτυο της ΠΔΕ καθώς κάτι τέτοιο θα ήταν πέρα από τους σκοπούς της παρούσας διατριβής. / This master thesis aims to study road accidents and identify hotspots at Western Greece Region. First, accidents occurred during 2000 - 2012 at Achaia, Aitoloakarnania and Ilia prefectures were recorded. Each record includes date, time, number of fatalities and injuries, specific location, road and environmental conditions at the moment of the accident. Collected data were grouped and analyzed statistically. Then after reviewing several numerical and statistical methods for hazardous location identification, a selection of methods was made and were applied in order to identify hotspots for some parts of the national road network. Subsequently, conclusions about the condition of the Western Greece Region road network, and the causes of accidents were derived. Finally, proposals for treatment and future research were made.