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 establish a theoretical limit for the identification of P0 in a class of epidemic models. We evaluate our method against different epidemic models on both synthetic and a real-world contact network considering a disease with history and characteristics of COVID-19. % 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.

本研究使用图神经网络来定位疫情的源头，实验表明GNN可以不需要对疫情动态或其参数进行显式输入就可以接近于理论界限的准确性，在任意图拓扑上推理速度比传统方法快100倍以上。同时研究发现疫情的传播就像一个倒计时，强调了及早进行联系追踪的重要性，否则在某个时间点之后就无法准确恢复源头。

寻找零号病人：使用图神经网络学习传染源