Deep Demixing: Reconstructing the Evolution of Network Epidemics
This work addresses the challenge of inferring disease spread dynamics in network epidemiology, which is incremental as it builds on graph autoencoder methods for a specific application.
The authors tackled the problem of reconstructing the complete propagation path of epidemics over networks from partial or aggregated temporal data, without assuming knowledge of the epidemic model, and demonstrated the accuracy and generalizability of their deep demixing model across synthetic and real-world networks.
We propose the deep demixing (DDmix) model, a graph autoencoder that can reconstruct epidemics evolving over networks from partial or aggregated temporal information. Assuming knowledge of the network topology but not of the epidemic model, our goal is to estimate the complete propagation path of a disease spread. A data-driven approach is leveraged to overcome the lack of model awareness. To solve this inverse problem, DDmix is proposed as a graph conditional variational autoencoder that is trained from past epidemic spreads. DDmix seeks to capture key aspects of the underlying (unknown) spreading dynamics in its latent space. Using epidemic spreads simulated in synthetic and real-world networks, we demonstrate the accuracy of DDmix by comparing it with multiple (non-graph-aware) learning algorithms. The generalizability of DDmix is highlighted across different types of networks. Finally, we showcase that a simple post-processing extension of our proposed method can help identify super-spreaders in the reconstructed propagation path.