融合机制与数据的分阶段疫情预测模型，提升长短周期预测精度。
MP-PINN: A Multi-Phase Physics-Informed Neural Network for Epidemic Forecasting
- 将传播机制嵌入神经网络，分阶段动态更新模型参数
- 在新冠各波次数据上，短长期预测均优于纯数据或纯模型方法
- 适合需要兼顾机制理解与数据适应性的疫情预测场景
疫情等时间过程的预测往往不仅依赖观测时间序列数据，尤其在疫情初期数据有限时更为明显。传统方法采用如SIR类的机理模型，对传播过程做强假设，通常以少量微分方程表示；数据驱动方法如深度神经网络不依赖假设，可更细致捕捉生成过程，但因数据限制难以进行长期预测。本文提出一种新型混合方法MP-PINN（多阶段物理信息神经网络），将传播机制嵌入神经网络，并允许其随时间分阶段更新，以反映政策干预带来的动态变化。在新冠各波次数据上的实验表明，MP-PINN在短期和长期预测上均显著优于纯数据驱动或纯模型驱动方法。
原文摘要 · Abstract (English)
Forecasting temporal processes such as virus spreading in epidemics often requires more than just observed time-series data, especially at the beginning of a wave when data is limited. Traditional methods employ mechanistic models like the SIR family, which make strong assumptions about the underlying spreading process, often represented as a small set of compact differential equations. Data-driven methods such as deep neural networks make no such assumptions and can capture the generative process in more detail, but fail in long-term forecasting due to data limitations. We propose a new hybrid method called MP-PINN (Multi-Phase Physics-Informed Neural Network) to overcome the limitations of these two major approaches. MP-PINN instils the spreading mechanism into a neural network, enabling the mechanism to update in phases over time, reflecting the dynamics of the epidemics due to policy interventions. Experiments on COVID-19 waves demonstrate that MP-PINN achieves superior performance over pure data-driven or model-driven approaches for both short-term and long-term forecasting.
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