The fast transmission rate of COVID-19 worldwide has made this virus the most important challenge of year 2020. Many mitigation policies have been imposed by the governments at different regional levels (country, state, county, and city) to stop the spread of this virus. Quantifying the effect of such mitigation strategies on the transmission and recovery rates, and predicting the rate of new daily cases are two crucial tasks. In this paper, we propose a hybrid modeling framework which not only accounts for such policies but also utilizes the spatial and temporal information to characterize the pattern of COVID-19 progression. Specifically, a piecewise susceptible-infected-recovered (SIR) model is developed while the dates at which the transmission/recover rates change significantly are defined as "break points" in this model. A novel and data-driven algorithm is designed to locate the break points using ideas from fused lasso and thresholding. In order to enhance the forecasting power and to describe additional temporal dependence among the daily number of cases, this model is further coupled with spatial smoothing covariates and vector auto-regressive (VAR) model. The proposed model is applied to several U.S. states and counties, and the results confirm the effect of "stay-at-home orders" and some states' early "re-openings" by detecting break points close to such events. Further, the model provided satisfactory short-term forecasts of the number of new daily cases at regional levels by utilizing the estimated spatio-temporal covariance structures. They were also better or on par with other proposed models in the literature, including flexible deep learning ones. Finally, selected theoretical results and empirical performance of the proposed methodology on synthetic data are reported which justify the good performance of the proposed method.
翻译:全世界COVID-19的快速传播率使这一病毒成为2020年最重要的挑战。不同区域(国家、州、州和城市)各级政府都实施了许多缓解政策,以阻止这种病毒的传播。量化这种减缓战略对传播率和回收率的影响,并预测每日新病例的发生率,这是两项关键任务。在本文件中,我们提出了一个混合模型框架,不仅说明这种政策,而且还利用空间和时间信息来说明COVID-19进展模式的特点。具体地说,一个零星的易感感染(SIR)模型(SIR)被开发出来,而传输/回收率变化的日期则被大大定义为该模型的“起点”和回收率的传播率。一个新的和数据驱动的算法旨在利用连接的拉索和起始点的构想来定位断点。为了提高预测力和描述每日拟议案件数量之间的额外时间依赖性,这一模型与空间平滑动和矢量的自动反向回移(VAR)模型(SIR)结构,同时将传输/恢复率率率大幅修正的日期定义为“定期测算结果”,而拟议的模型也用于若干州和州间测测测测测测结果。提议的连续测测测测结果,还提供了某些测结果,这些结果,这些结果,还提供了一些州。S- St断结果,还提供了某些测测测测测测测测测测结果,还数据,还提供了某些测测测测测结果。