Epidemic spreading modelling
I have developed several models for epidemic spreading that highlight how realistic mechanisms reshape classical epidemic wisdom.
Higher-order epidemic spreading
I proposed a hypergraph model for epidemic spreading that jointly captures the heterogeneity of infectious environments and individual participation. I showed that heterogeneous exposure and minimal infective dose induce a universal nonlinear infection kernel, leading to discontinuous transitions, super-exponential spread, and hysteresis.
Digital contact tracing
I analysed digital contact tracing and demonstrated a highly nonlinear relationship between app adoption and the epidemic threshold, providing one of the first theoretical assessments of app-based mitigation strategies.
Time-dependent branching processes
I studied the effect of time-dependent infectivity induced by containment measures in a stochastic branching-process framework, showing how temporal modulation and stochasticity jointly control critical exponents and offering an explanation for power-law growth regimes observed during COVID-19.
Related publications
- Critical time-dependent branching process modelling epidemic spreading with containment measures, Journal of Physics A: Mathematical and Theoretical, 2022 — H. Sun, I. Kryven, G. Bianconi
- Universal nonlinear infection kernel from heterogeneous exposure on higher-order networks, Physical Review Letters, 2021 — G. St-Onge, H. Sun, A. Allard, L. Hébert-Dufresne, G. Bianconi
- Message-passing approach to epidemic tracing and mitigation with apps, Physical Review Research, 2021 — G. Bianconi, H. Sun, G. Rapisardi, A. Arenas
