用深度学习捕捉疾病爆发前的早期信号，抗干扰能力强。
Deep Learning for Disease Outbreak Prediction: A Robust Early Warning Signal for Transcritical Bifurcations
- 用动态系统模拟新病行为，结合噪声数据训练模型
- 在流感与新冠真实数据上表现优于已有方法
- 适合应对突发疫情预警，尤其在数据杂乱时
早期预警信号对预防疾病演变为大流行至关重要。尽管新发疾病表现出独特行为，但从动力系统角度看常具共性特征。然而，疫情数据常受多种噪声污染，给时间序列分类任务带来挑战。本研究采用该领域表现最佳的深度学习模型，构建鲁棒的早期预警系统。通过两个模拟数据集训练：一个模拟具有随机多项式项的动力系统以刻画新病行为，另一个模拟噪声诱导的疾病动态以应对测量误差。模型性能在多种疾病模型的模拟数据及流感、新冠等真实数据上评估。结果表明，所提模型在各类场景下均显著优于现有方法，能有效提供疫情暴发前的预警信号。该研究将深度学习进展与噪声环境下的可靠预警能力相结合，对应对新兴传染病危机具有重要应用价值。
原文摘要 · Abstract (English)
Early Warning Signals (EWSs) are vital for implementing preventive measures before a disease turns into a pandemic. While new diseases exhibit unique behaviors, they often share fundamental characteristics from a dynamical systems perspective. Moreover, measurements during disease outbreaks are often corrupted by different noise sources, posing challenges for Time Series Classification (TSC) tasks. In this study, we address the problem of having a robust EWS for disease outbreak prediction using a best-performing deep learning model in the domain of TSC. We employed two simulated datasets to train the model: one representing generated dynamical systems with randomly selected polynomial terms to model new disease behaviors, and another simulating noise-induced disease dynamics to account for noisy measurements. The model's performance was analyzed using both simulated data from different disease models and real-world data, including influenza and COVID-19. Results demonstrate that the proposed model outperforms previous models, effectively providing EWSs of impending outbreaks across various scenarios. This study bridges advancements in deep learning with the ability to provide robust early warning signals in noisy environments, making it highly applicable to real-world crises involving emerging disease outbreaks.
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