Zeng Xiaoshu, Ghanem Roger
Viterbi School of Engineering, University of Southern California, 210 KAP Hall, Los Angeles, CA 90089 USA.
Comput Mech. 2020;66(5):1179-1193. doi: 10.1007/s00466-020-01911-4. Epub 2020 Aug 29.
The COVID-19 pandemic has captivated scientific activity since its early days. Particular attention has been dedicated to the identification of underlying dynamics and prediction of future trend. In this work, a switching Kalman filter formalism is applied on dynamics learning and forecasting of the daily new cases of COVID-19. The main feature of this dynamical system is its ability to switch between different linear Gaussian models based on the observations and specified probabilities of transitions between these models. It is thus able to handle the problem of hidden state estimation and forecasting for models with non-Gaussian and nonlinear effects. The potential of this method is explored on the daily new cases of COVID-19 both at the state-level and the country-level in the US. The results suggest a common disease dynamics across states that share certain features. We also demonstrate the ability to make short to medium term predictions with quantifiable error bounds.
自新冠疫情初期以来，它就吸引了科研活动。人们特别关注其潜在动态的识别以及未来趋势的预测。在这项工作中，一种切换卡尔曼滤波器形式被应用于新冠疫情每日新增病例的动态学习和预测。这个动态系统的主要特点是它能够根据观测值以及这些模型之间特定的转移概率，在不同的线性高斯模型之间进行切换。因此，它能够处理具有非高斯和非线性效应模型的隐藏状态估计和预测问题。在美国，我们在州级和国家级层面针对新冠疫情每日新增病例探讨了该方法的潜力。结果表明，不同州之间存在具有某些共同特征的疾病动态。我们还展示了做出具有可量化误差范围的短期到中期预测的能力。
