Prediction of the Effects of Epidemic Spreading with Graph Neural Networks

Abstract

Understanding how information propagates in real-life complex networks yields a better understanding of dynamical processes such as misinformation or epidemic spreading. With the recent resurgence of graph neural networks as a powerful predictive methodology, many network properties can be studied in terms of their predictability and as such offer a novel view on the studied process, with the direct application of fast predictions that are complementary to resource-intensive simulations. We investigated whether graph neural networks can be used to predict the effect of an epidemic, should it start from a given individual (patient zero). We reformulate this problem as node regression and demonstrate the high utility of network-based machine learning for a better understanding of the spreading effects. By being able to predict the effect of a given individual being the patient zero, the proposed approach offers potentially orders of magnitude faster risk assessment and potentially aids the adopted epidemic spreading analysis techniques.