Bayesian models and methods to estimate age-specific infectious disease transmission dynamics: integrating disease surveillance time series, mobility, and vaccination data

Music City Center 

My PhD research focuses on developing Bayesian methods for inferring structured mathematical infectious disease models within modern probabilistic computing languages to characterize disease spread. By capturing fine-scale transmission dynamics of multi-type epidemics, my work extends beyond traditional homogeneous models to account for complex, structured interactions across populations. Central to this approach, the framework provides a fully generative structured model for latent infections and their resulting observations - including deaths, recorded cases, and seroprevalence surveys - which are essential for quantifying uncertainty in epidemic trajectories and guiding targeted interventions in population subgroups. A key contribution is the development of an age- and time-specific Bayesian transmission model that links real-time mobile phone mobility data to COVID-19 mortality data through human contact patterns and then uses this rich relationship to identify demographic groups driving SARS-CoV-2 transmission in the United States. These findings, published in Science and presented at leading conferences, provide crucial insights for targeted public health interventions and vaccination strategies. To address computational challenges in Bayesian inference for large-scale epidemic models, I introduced a non-parametric spatial prior using a low-rank, two-dimensional Gaussian process projected by regularized B-splines. This method enhanced computational efficiency while preserving predictive accuracy. Embedded within a Bayesian hierarchical framework, it was used to estimate the impact of SARS-CoV-2 vaccination coverage on mortality trends in the United States. This research was published in Bayesian Analysis and presented at the ISBA World Meeting.