Politecnico di Torino, DISAT, 10129, Turin, Italy.
Sci Rep. 2022 Nov 16;12(1):19673. doi: 10.1038/s41598-022-20898-x.
The reconstruction of missing information in epidemic spreading on contact networks can be essential in the prevention and containment strategies. The identification and warning of infectious but asymptomatic individuals (i.e., contact tracing), the well-known patient-zero problem, or the inference of the infectivity values in structured populations are examples of significant epidemic inference problems. As the number of possible epidemic cascades grows exponentially with the number of individuals involved and only an almost negligible subset of them is compatible with the observations (e.g., medical tests), epidemic inference in contact networks poses incredible computational challenges. We present a new generative neural networks framework that learns to generate the most probable infection cascades compatible with observations. The proposed method achieves better (in some cases, significantly better) or comparable results with existing methods in all problems considered both in synthetic and real contact networks. Given its generality, clear Bayesian and variational nature, the presented framework paves the way to solve fundamental inference epidemic problems with high precision in small and medium-sized real case scenarios such as the spread of infections in workplaces and hospitals.
在接触网络上传播的疫情中，缺失信息的重建对于预防和控制策略至关重要。识别和警告具有传染性但无症状的个体（即接触者追踪）、众所周知的“零号病人”问题，或推断结构化人群中的传染性值，这些都是重要的疫情推断问题。由于可能的疫情传播数量随着涉及的个体数量呈指数级增长，而只有极少数与观察结果（例如医学检测）相符，因此接触网络中的疫情推断带来了难以置信的计算挑战。我们提出了一种新的生成式神经网络框架，该框架可以学习生成与观察结果最相符的最可能的感染传播。在所考虑的所有问题中，无论是在合成接触网络还是真实接触网络中，该方法在所有问题上的表现都优于（在某些情况下，显著优于）或可与现有方法相媲美。鉴于其通用性、明确的贝叶斯和变分性质，所提出的框架为解决小型和中型真实场景中的基本疫情推断问题铺平了道路，例如工作场所和医院中的传染病传播。
