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 will employ a networked structured population framework, where interactions are modeled between communities or subpopulations rather than at an individual level. Using this coarse-grained approach, I will demonstrate the adaptability of this framework for multiple types of contagion processes and provide both theoretical and applied results.
In my first project, 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 find that the presence of a subpopulation structure fundamentally alters the contagion dynamics, highlighting the importance of understanding how the social structure in both the physical and virtual worlds affects the emergence of contagion phenomena. In my second project, I studied the effectiveness of travel restrictions against 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 project, I will provide a comprehensive analysis of the cryptic transmission phase and the following 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.
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