融合流行病学机制与深度学习，提升结核病暴发的时空预测准确率。
Epidemic-guided deep learning for spatiotemporal forecasting of Tuberculosis outbreak
- 基于改进的网络化SIR模型结合图拉普拉斯扩散，捕捉传播动态与人口流动。
- 在中日47个县市和31个省份数据上实现短中期精准预测，误差显著降低。
- 适合公共卫生部门用于疫情预警和干预策略制定，兼具理论与实用价值。
结核病（TB）仍是全球重大健康挑战，其传播具有复杂的时空特性，受人口流动与行为变化影响。本文提出一种流行病引导的深度学习框架（EGDL），将流行病学原理与先进深度学习技术融合，以增强结核病暴发的早期预警与干预能力。该框架基于改进的网络化易感-感染-康复（MN-SIR）模型，引入饱和发病率与图拉普拉斯扩散项，刻画长期传播动态与区域人口流动特征。通过马尔可夫链蒙特卡洛方法进行贝叶斯推断，严格估计模型参数。理论分析利用比较原理与格林公式，证明了无病平衡点与地方性平衡点的全局稳定性。在此基础上，设计两种融合机制输出的预测架构：EGDL-Parallel与EGDL-Series，将MN-SIR模型结果嵌入深度神经网络，有效缓解数据驱动方法的过拟合问题，并过滤监测数据中的噪声，提升预测可靠性。在涵盖日本47个县级单位及中国大陆31个省份的结核病发病率数据上进行实验，验证了该方法在多个时间尺度（短至中期）上的鲁棒性与高精度，展现出良好的跨区域泛化能力。
原文摘要 · Abstract (English)
Tuberculosis (TB) remains a formidable global health challenge, driven by complex spatiotemporal transmission dynamics and influenced by factors such as population mobility and behavioral changes. We propose an Epidemic-Guided Deep Learning (EGDL) approach that fuses mechanistic epidemiological principles with advanced deep learning techniques to enhance early warning systems and intervention strategies for TB outbreaks. Our framework is built upon a modified networked Susceptible-Infectious-Recovered (MN-SIR) model augmented with a saturated incidence rate and graph Laplacian diffusion, capturing both long-term transmission dynamics and region-specific population mobility patterns. Compartmental model parameters are rigorously estimated using Bayesian inference via the Markov Chain Monte Carlo approach. Theoretical analysis leveraging the comparison principle and Green's formula establishes global stability properties of the disease-free and endemic equilibria. Building on these epidemiological insights, we design two forecasting architectures, EGDL-Parallel and EGDL-Series, that integrate the mechanistic outputs of the MN-SIR model within deep neural networks. This integration mitigates the overfitting risks commonly encountered in data-driven methods and filters out noise inherent in surveillance data, resulting in reliable forecasts of real-world epidemic trends. Experiments conducted on TB incidence data from 47 prefectures in Japan and 31 provinces in mainland China demonstrate that our approach delivers robust and accurate predictions across multiple time horizons (short to medium-term forecasts), supporting its generalizability across regions with different population dynamics.
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