Predicting disease infection pathways

We have just published a paper at the prestigious Proceedings of the National Academy of Sciences Journal titled “Modeling stochastic processes in disease spread across a heterogeneous social system” by Kim, Paini, and Jurdak. The study infers probabilistic infection routes of a vector-borne disease, by modeling internal dynamics of metapopulations driven by human mobility as multivariate stochastic processes. In this way, the proposed model uncovers the self-excitation and mutual excitation nature of disease spread across a heterogeneous social system with rich context. The model is a general extension of networked Hawkes processes, providing flexibilities to add constraints (presence of diffusion medium) and to use domain knowledge (cross-metapopulation connectivity), enabling covering of direct and indirect diffusion processes such as contact-based and vector-borne disease spread. The model is readily applicable to a wide range of intragroup and intergroup diffusion processes in social and natural systems and can infer probabilistic causality between discrete event.

This work is part of the DiNeMo project.