AI 中文总结
该研究针对基孔肯雅热动力学预测难题，提出结合SINDy与EnKF的混合数据驱动框架，可提升含噪声、部分观测数据下的预测精度与未观测轨迹重构能力。
AI 中文摘要
当前用于基孔肯雅热病毒（CHIKV）传播动力学的机制模型依赖不确定参数或部分观测数据，这一限制阻碍了理论模型在理解和预测疾病传播中的应用。本文提出一种混合数据驱动模型框架，将非线性动力学稀疏识别（SINDy）与集合卡尔曼滤波（EnKF）结合以实现序贯数据同化。数值实验表明，该方法可提升预测精度，并在部分可观测（真实世界流行病学监测的常见约束）条件下良好重构未观测轨迹。在无噪声条件下，SINDy可应用于流行病轨迹以恢复潜在方程；但单独使用SINDy对噪声高度敏感，会产生虚假项且性能较差。因此，本文将识别过程嵌入EnKF框架，该框架同化含噪声观测值以校正SINDy衍生模型的预测状态，并推断未观测状态变量。
英文摘要
Current mechanistic models for the transmission dynamics of the Chikungunya virus (CHIKV) rely on uncertain parameters or partially observed data. This limitation challenges the use of theoretical models for understanding and forecasting disease spread. Here we present a hybrid, data-driven model framework that combines Sparse Identification of Nonlinear Dynamics (SINDy) with the Ensemble Kalman Filter (EnKF) for sequential data assimilation. Our numerical experiments show that this approach improves prediction accuracy and provides a good reconstruction of unobserved trajectories under partial observability, a common constraint in real-world epidemiological surveillance. SINDy can be applied to epidemic trajectories, recovering the underlying equations in noise-free conditions. However, standalone SINDy is highly sensitive to noise, leading to spurious terms and poor performance. Hence, we embed the identification procedure within an EnKF framework, which assimilates noisy observations to correct forecast states from the SINDy-derived model and to infer unobserved state variables.
