Speaker
Description
Traditional analyses of epidemic spreading on networks often rely on spectral properties, steady-state approximations, or aggregate measures of final outbreak size. While these metrics provide essential insights into threshold behaviour, they frequently obscure the granular mechanistic processes by which individual interactions and temporal variations drive population-level dynamics. This work utilizes the Generalized Epidemic Mean-Field (GEMF) framework—an N-intertwined deterministic approximation—to extend the Susceptible-Infected-Removed (SIR) model across structured populations. By mapping the stochastic Markovian process onto a system of 3N nonlinear differential equations, we investigate the spreading process within both stationary and temporal empirical contact networks. We extend the standard GEMF formulation to capture the cumulative edge flux: a time-integrated measure of the probability-weighted infection transmission across every discrete contact in the network. This approach allows for a precise decomposition of the "force of infection," identifying how specific individuals and their localized contact patterns contribute to the overall trajectory of a disease. This granular methodology provides a rigorous framework for exploring the interplay between individual behaviour and population-scale spreading, offering new avenues for the design of targeted, behaviour-aware intervention strategies.
