Cross-scale dynamics of infectious disease

Abstract

Recent emergence and re-emergence of infectious disease pathogens have caused a significant disruption to global society, highlighting the importance of predicting future outbreaks. These epidemics have also provided valuable data for answering broader questions about population and evolutionary biology. In this thesis, I use various computational tools to understand how individual-level changes in immunity and behavior translates to population-level dynamics of disease transmission. The thesis consists of three sections, each of which contains separate published papers. In the first, I present a framework for linking individual-level time scale of transmission to population-level spread. I begin by laying out general theory for quantifying changes in generation- and serial-interval distributions (Chapter 2). Then, I apply this framework to comparing transmission dynamics of Delta and Omicron SARS-CoV-2 variants (Chapter 3). In the second, I analyze population-level dynamics of Enterovirus D68 by leveraging data sets from novel surveillance platforms. I characterize spatiotemporal dynamics of Enterovirus D68 in the US, revealing the role of immunity in driving biennial outbreak patterns and further demonstrating strong correlations with outbreak patterns of acute flaccid myelitis, a novel neurological disease that recently emerged (Chapter 4). Then, I study the effects of pandemic intervention measures on the dynamics of EV-D68 and discuss implications for future outbreaks (Chapter 5). Finally, I present an analysis of SARS-CoV-2 outbreak on Princeton University campus (Chapter 6). This final chapter builds on modeling work that I had done during the first two years of the pandemic to guide university's testing and isolation guidelines. The study highlights the role of community transmission and behavioral changes in driving epidemic patterns on campus.