Bayesian and Spatiotemporal Modeling of Infectious Diseases and Population Health

Abstract

Infectious disease outbreak detection is a critical component of public health surveillance. However, the data and methods available for this task vary, and there is limited guidance on which method to apply to a given dataset. Additionally, outbreak detection and public health rate estimation rely on accurate population denominators, yet in the U.S., it is unclear which data sources provide the most reliable estimates. This dissertation addresses both issues by evaluating outbreak detection methods for syndromic and wastewater-based surveillance and by developing a model to estimate U.S. county populations. In Chapter 1, we present a simulation study evaluating spatio-temporal models for syndromic surveillance in low-resource settings. Conventional syndromic surveillance methods face challenges in handling missing data and often do not leverage spatio-temporal structure. We compare a baseline syndromic surveillance model, a frequentist spatio-temporal model, and a Bayesian spatio-temporal conditional autoregressive (CAR) model. The Bayesian CAR model consistently achieves high specificity across simulations, underscoring the importance of spatio-temporal modeling in syndromic surveillance. In Chapter 2, we introduce the Spatially-Weighted Ensemble for Estimation of Populations (SWEEP), a Bayesian ensemble model that combines the American Community Survey (ACS), Population Estimates Program (PEP), and WorldPop (WP) to generate intercensal population estimates. SWEEP uses spatially varying weights that adapt to geographic patterns in product accuracy. Using 2019 product estimates to predict 2020 census counts, SWEEP improves population estimates, particularly for the American Indian and Alaska Native (AIAN) population, and reveals systematic geographic variation in data accuracy. These findings demonstrate the potential of spatially adaptive ensemble modeling to improve population estimates and support more equitable disease and mortality rate estimation. In Chapter 3, we develop a wastewater-based outbreak detection method using an exponential growth model and evaluate its performance relative to clinically-defined outbreaks. Applied to countylevel COVID-19 data, this method outperforms a reproductive number (Rt)-based approach. Detection performance improves with spatial aggregation yet declines in extreme temperatures, high humidity, and after 2021. These results suggest that wastewater surveillance can reliably detect outbreaks, though its performance varies with environmental context and its evaluation depends on the quality of reference clinical data.Biostatistic