B0503
Title: Stochastic epidemic models on dynamic contact networks
Authors: Fan Bu - University of Michigan (United States) [presenting]
Allison Aiello - Columbia University (United States)
Jason Xu - Duke University (United States)
Alexander Volfovsky - Duke University (United States)
Abstract: Infectious disease transmission relies on interpersonal contact networks, but traditional epidemic models often assume a random-mixing population where all individuals are equally likely to get infected. We seek to develop a more realistic and generalized stochastic epidemic model that considers transmission over dynamic networks. We propose a joint epidemic-network model through a continuous-time Markov chain such that disease transmission is constrained by the contact network structure, and network evolution is, in turn, influenced by individual disease statuses. To accommodate partial epidemic observations commonly seen in real-world data, we design efficient inference algorithms through data augmentation that leverages dynamic network features and infection mechanisms. At the same time, our approach can account for individual heterogeneity and explore intervention effects on disease transmission. Experiments on both synthetic and real datasets demonstrate that our inference method can accurately and efficiently recover model parameters and provide valuable insight into epidemic data in the presence of unobserved disease episodes.
