Deep Demixing: Reconstructing the Evolution of Epidemics Using Graph Neural Networks

TL;DR

This paper introduces DDmix, a graph neural network-based method for reconstructing complete epidemic evolution from partial data, leveraging network topology without needing detailed epidemic models.

Contribution

The paper presents DDmix, a novel graph conditional variational autoencoder that captures epidemic dynamics from partial observations, generalizing across different network structures and epidemic scenarios.

Findings

DDmix accurately reconstructs epidemic spreads on synthetic and real networks.

DDmix outperforms non-graph-aware algorithms in reconstruction tasks.

The model generalizes well to unseen epidemic scenarios.

Abstract

We study the temporal reconstruction of epidemics evolving over networks. Given partial or aggregated temporal information of the epidemic, our goal is to estimate the complete evolution of the spread leveraging the topology of the network but being agnostic to the precise epidemic model. We overcome this lack of model awareness through a data-driven solution to the inverse problem at hand. In particular, we propose DDmix, a graph conditional variational autoencoder that can be trained from past epidemic spreads and whose latent space seeks to capture key aspects of the underlying (unknown) spreading dynamics. We illustrate the accuracy and generalizability of DDmix and compare it with non-graph-aware learning algorithms through numerical experiments on epidemic spreads simulated on synthetic and real-world networks.