Principled Modeling of Contagion Dynamics Across Scales
Guillaume St-Onge
Postdoctoral Research Associate, Network Science Institute, Northeastern University
Past Talk
Hybrid talk
Thursday
Feb 15, 2024
Watch video
11:00 am
Roux-136-Diamond, The Roux Institute, 100 Fore St, Portland, ME 04101
Roux-136-Diamond, The Roux Institute, 100 Fore St, Portland, ME 04101
Virtual
Roux-136-Diamond, The Roux Institute, 100 Fore St, Portland, ME 04101
177 Huntington Ave.
11th floor
Online
Register here
Contagion processes, be it the unfolding of an infectious disease epidemic or the dissemination of misinformation in a population, are multiscale phenomena that spread through individuals' interactions. At the finest resolution, these interactions may take the form of a network interconnecting each individual, rich with heterogeneities, and may vary in time. On a larger scale, entire subpopulations—which might range from a small city to a country—influence each other because of human mobility, notably shaped by the global air travel network. In this presentation, we will start at the individual level and discuss the modeling of contagion on networks, emphasizing the important roles of heterogeneity, group structure, and modeling assumptions in shaping the properties of spreading processes. In the second part, we will move to a population-level description of contagions, allowing us to model spreading processes on a global scale. Achieving realistic modeling at this scale, however, imposes a significant computational burden. To circumvent this issue, we introduce a novel metapopulation framework that leverages probability generating functions. This approach notably enables us to rigorously model and quantify the performance of a global wastewater surveillance system at airports for detecting emerging pathogens. Finally, we will discuss future directions for extending this computational platform, how to bridge the seemingly disconnected modeling scales, and how this research aligns with a broader vision to improve population health and better prepare for the next pandemic.
About the speaker
About the speaker
Dr. Guillaume St-Onge is a Postdoctoral Research Associate at the Network Science Institute in Boston, specializing in the development and application of mathematical models of contagion dynamics. He holds a Ph.D. in physics from Université Laval in Quebec, where he developed mathematical models of contagion on networks to explore the impact of group structures, cooperative behavior, and adaptation on epidemic outcomes. Recently, Dr. St-Onge has directed his efforts toward more realistic modeling of infectious diseases through metapopulation network approaches, aiming to provide useful insights for public health. He has notably developed a new computational platform that utilizes probability generating functions for computational efficiency, proving especially useful in the modeling of travel-based genomic surveillance at airports.
Dr. Guillaume St-Onge is a Postdoctoral Research Associate at the Network Science Institute in Boston, specializing in the development and application of mathematical models of contagion dynamics. He holds a Ph.D. in physics from Université Laval in Quebec, where he developed mathematical models of contagion on networks to explore the impact of group structures, cooperative behavior, and adaptation on epidemic outcomes. Recently, Dr. St-Onge has directed his efforts toward more realistic modeling of infectious diseases through metapopulation network approaches, aiming to provide useful insights for public health. He has notably developed a new computational platform that utilizes probability generating functions for computational efficiency, proving especially useful in the modeling of travel-based genomic surveillance at airports.