用时间序列数据同时推断疫情传播网络与参数，突破传统单向假设局限。
Learning graph topology from metapopulation epidemic encoder-decoder
- 设计双编码器-解码器架构，从疫情数据反推子群体间移动网络
- 在随机与真实网络上均优于现有方法，多病原数据显著提升精度
- 适合从事流行病建模、网络推断的科研人员使用
元种群流行病模型是研究大规模疫情传播的重要工具。由于流行病追踪数据有限，难以同时推断模型的核心要素——流行病参数和子群体间的移动网络。以往研究通常假设一方已知以推断另一方，但二者联合推断问题尚未解决。本文提出两种基于编码器-解码器的深度学习架构，可在有无流行病参数假设的情况下，仅从时间序列数据中推断元种群移动网络。在多种随机与实证移动网络上的评估表明，该方法性能优于当前最优的拓扑推断方法。此外，引入更多病原体数据可显著提升推断效果。本研究建立了一个稳健的框架，实现流行病参数与网络结构的联合推断，填补了疾病传播建模中的长期空白。
原文摘要 · Abstract (English)
Metapopulation epidemic models are a valuable tool for studying large-scale outbreaks. With the limited availability of epidemic tracing data, it is challenging to infer the essential constituents of these models, namely, the epidemic parameters and the relevant mobility network between subpopulations. Either one of these constituents can be estimated while assuming the other; however, the problem of their joint inference has not yet been solved. Here, we propose two encoder-decoder deep learning architectures that infer metapopulation mobility graphs from time-series data, with and without the assumption of epidemic model parameters. Evaluation across diverse random and empirical mobility networks shows that the proposed approach outperforms the state-of-the-art topology inference. Further, we show that topology inference improves dramatically with data on additional pathogens. Our study establishes a robust framework for simultaneously inferring epidemic parameters and topology, addressing a persistent gap in modeling disease propagation.
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