Data-driven deep learning provides efficient algorithms for parameter identification of epidemiology models. Unlike the constant parameters, the complexity of identifying time-varying parameters is largely increased. In this paper, a variant of physics-informed neural network is adopted to identify the time-varying parameters of the Susceptible-Infectious-Recovered-Deceased model for the spread of COVID-19 by fitting daily reported cases. The learned parameters are verified by utilizing an ordinary differential equation solver to compute the corresponding solutions of this compartmental model. The effective reproduction number based on these parameters is calculated. Long Short-Term Memory neural network is employed to predict the future weekly time-varying parameters. The numerical simulations demonstrate that PINN combined with LSTM yields accurate and effective results.

数据驱动的深度学习为流行病学模型的参数识别提供了有效的算法。与常量参数不同，识别时变参数的复杂性大大增加。在本文中，采用物理信息神经网络的一种变体，通过拟合每日报告的病例来识别 COVID-19 传播的易感-感染-恢复-死亡模型的时变参数。通过利用常微分方程求解器计算该分区模型的相应解来验证学习参数。计算基于这些参数的有效再现数。长短期记忆神经网络用于预测未来每周随时间变化的参数。