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

• Universal Nonlinear Infection Kernel from Heterogeneous Exposure on Higher-Order Networks — Physical Review Letters
• Message-passing approach to epidemic tracing and mitigation with apps — Physical Review Research
• Critical time-dependent branching process modelling epidemic spreading with containment measures — Journal of Physics A