NetIn-Talk by Pourya Toranj Simin

The homogeneity assumption in epidemiology assumes that all elements within a system share similar characteristics, which can oversimplify models and predictions. However, real-world heterogeneity significantly influences outcomes, especially in epidemiology, where various sources of heterogeneity impact infectious disease circulation. In this presentation, I will discuss the impact of heterogeneity in social contact patterns on pandemic dynamics, focusing on COVID-19 variants. Early in the pandemic, variants of concern (VOCs) emerged, with increased transmissibility and immune escape, but competition dynamics varied across locations. We investigated how social contact networks and degree distribution heterogeneity affect variant co-circulation. Computational modeling revealed that the advantage of interacting variants is sensitive to population properties, with high dispersion in degree distribution slowing down the emergence of more transmissible variants while accelerating the diffusion of variants escaping immunity. I will also discuss how we could validate our findings through empirical analysis using proxy data on contact networks.