26 papers
This paper presents an agent-based model within the EpiCast framework that simulates the coupled dynamics of disease and fear transmission, demonstrating how non-local fear spread via media combined with strong behavioral responses can generate multiple epidemic waves and highlighting the critical importance of accounting for these feedback loops in outbreak management.
This paper develops a method to derive simple, analytically explicit upper or lower bounds for the survival probability of beneficial mutations in supercritical Galton-Watson processes by approximating their generating functions with fractional linear ones, and applies these results to model the evolution of quantitative traits under directional selection in finite populations.
This paper presents a hybrid discrete-continuum modeling framework that couples a stochastic agent-based model with partial differential equations to investigate the spatial dynamics of oncolytic virotherapy and immune system interactions, revealing that while both models generally agree, stochastic effects and the timing of immune responses are critical factors that can significantly influence therapeutic efficacy.
This paper develops a mean-field game framework for SEIR epidemics on heterogeneous networks to characterize Nash equilibrium contact policies, revealing how the incubation period delays precautionary behavior and influences outbreak severity through a coupled Hamilton-Jacobi-Bellman/Kolmogorov system.
This paper introduces a stochastic agent-based model of opinion dynamics incorporating heterogeneous issue weights and both attractive and repulsive social forces, revealing that even minor issues can destabilize consensus and drive polarization or pluralism depending on similarity thresholds, while offering symmetry-based insights and strategies to mitigate polarization through diversified discourse.
This paper introduces a combined theoretical and data-driven framework to predict how structural complexity and delayed feedback jointly trigger oscillations in complex networks, a theory validated by electronic circuit experiments and enhanced by reservoir computing for accurate onset prediction from time-series data.