Finding Patient Zero: Learning Contagion Source with Graph Neural Networks

Chintan Shah, Nima Dehmamy, Nicola Perra, Matteo Chinazzi, Albert-Laszlo Barabasi, Alessandro Vespignani, Rose Yu

Keywords: contagion dynamics, theory of graph neural networks, epidemic modeling

Abstract:

Locating the source of an epidemic, or patient zero (P0), can provide critical insights into the infection's transmission course and allow efficient resource allocation.
Existing methods use graph-theoretic centrality measures and expensive message-passing algorithms, requiring knowledge of the underlying dynamics and its parameters.
In this paper, we revisit this problem using graph neural networks (GNNs) to learn P0.
We observe that GNNs can identify P0 close to the theoretical bound on accuracy, without explicit input of dynamics or its parameters.
In addition, GNN is over 100 times faster than classic methods for inference on arbitrary graph topologies.
Our theoretical bound also shows that the epidemic is like a ticking clock, emphasizing the importance of early contact-tracing.
We find a maximum time after which accurate recovery of the source becomes impossible, regardless of the algorithm used.

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One-sentence Summary: Investigation and theoretical understanding for the behavior of GNN when using it to find patient zero in epidemic dynamics.

Reviewed Version (pdf): https://openreview.net/references/pdf?id=YpqPsQIFSM