2023/11/01 by Nikolaos Evangelou, Tianqi Cui, Evangelou, Nikolaos +7 · 1 citation
Environmental Science · Mathematics · Psychology · #Artificial Intelligence (cs.AI) #COVID-19 epidemiological studies #Dynamical Systems (math.DS) #Ecosystem dynamics and resilience #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Mental Health Research Topics #Populations and Evolution (q-bio.PE)
paper · pdf · doi:10.48550/arxiv.2311.00797
openalex publication_date 2023/11/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
We study the tipping point collective dynamics of an adaptive susceptible-infected-susceptible (SIS) epidemiological network in a data-driven, machine learning-assisted manner. We identify a parameter-dependent effective stochastic differential equation (eSDE) in terms of physically meaningful coarse mean-field variables through a deep-learning ResNet architecture inspired by numerical stochastic integrators. We construct an approximate effective bifurcation diagram based on the identified drift term of the eSDE and contrast it with the mean-field SIS model bifurcation diagram. We observe a subcritical Hopf bifurcation in the evolving network's effective SIS dynamics, that causes the tipping point behavior; this takes the form of large amplitude collective oscillations that spontaneously -- yet rarely -- arise from the neighborhood of a (noisy) stationary state. We study the statistics of these rare events both through repeated brute force simulations and by using established mathematical/computational tools exploiting the right-hand-side of the identified SDE. We demonstrate that such a collective SDE can also be identified (and the rare events computations also performed) in terms of data-driven coarse observables, obtained here via manifold learning techniques, in particular Diffusion Maps. The workflow of our study is straightforwardly applicable to other complex dynamics problems exhibiting tipping point dynamics.