Computational models of contagion processes in complex systems have a rich history that incorporates the diverse, cross-disciplinary work of social scientists, epidemiologists, computer scientists, mathematicians, and physicists. Initially, stylized models were derived to develop a general, mechanistic understanding of the underlying properties that govern contagion phenomena such as the diffusion of information, the adoption of a behavior, and the spread of an infectious disease. However, recently, the increased availability of highly detailed data that describes our social and physical connections has led to the construction of models that can capture the diverse, heterogeneities of complex socio-technical systems. At the core of these realistic models, we often find networks, which encode characteristics of our social and physical connections. In my dissertation I employ a networked, structured population framework where interactions are modeled between communities or subpopulations rather than at an individual level. With this coarse-grained approach, we can incorporate many properties often found in complex systems such as the effects of human mobility and time-varying behavioral patterns. The goal of this dissertation will aim to extend our understanding of contagion models within this framework and, in particular, show how we can adapt this framework to study both social and biological contagions.
In my first chapter, I characterize the dynamics of a classic rumor spreading model in two types of structured populations where one is modeled after spatially distributed systems and the other after interactions in a virtual community. I show that features observed in real-world systems can potentially alter the theoretical picture and understanding provided by only studying stylized models. Additionally, the modeling results suggest that successful information or rumor spreading is the result of a complex interaction between the intrinsic properties of the contagion process and the dynamics of interactions between subpopulations/communities. In my second chapter, I use a global metapopulation epidemic model to study the effectiveness of travel restrictions on the global dispersion of SARS-CoV-2 out of mainland China. I find that the travel restrictions implemented at the end of January and February 2020 alone would not be enough to contain the initial epidemic, and interventions aimed at reducing the risk of transmission provide the greatest benefit. The initial ineffectiveness of travel restrictions and other early containment measures allowed SARS-CoV-2 to quickly and cryptically propagate globally. In my final chapter, I use the same metpopulation modeling framework from the previous chapter to provide a comprehensive analysis of the cryptic transmission phase and the ensuing initial wave of the COVID-19 pandemic. Understanding how the early spreading dynamics of the pandemic unfolded will be crucial to researchers and policymakers as new infectious diseases and SARS-CoV-2 variants emerge.
Alessandro Vespignani (chair), Network Science Institute; Northeastern University
Brooke Foucault-Welles, Network Science Institute; Northeastern University
Samuel Scarpino, Rockefeller Foundation; Network Science Institute
Cécile Viboud, Fogarty International Center at the National Institutes of Health
Jessica is a fourth year PhD candidate working with Professor Alessandro Vespignani in the MOBS Lab. Her current research involves modeling how complex networks affect the spread of information, behavior, and infectious diseases. She received her bachelor’s degree from the University of North Carolina at Chapel Hill where she majored in Mathematics and Communication Studies.